diff --git a/docs/source/_data/modules/en.json b/docs/source/_data/modules/en.json
index cd7349f25f..12d70ad1af 100644
--- a/docs/source/_data/modules/en.json
+++ b/docs/source/_data/modules/en.json
@@ -10,7 +10,7 @@
"bfast_explorer": {
"description": "Performs analysis of Landsat Surface Reflectance time series pixel data using the BFAST algorithm.",
"tag": ["time-series"],
- "url": "https://raw.githubusercontent.com/sepal-contrib/bfast-explorer/master/md/tutorial.rst"
+ "url": "https://raw.githubusercontent.com/sepal-contrib/bfast-explorer/main/md/tutorial.rst"
},
"bfast_gpu": {
"description": "GPU implementation of the BFAST algorithm to analyse time series",
@@ -19,10 +19,7 @@
},
"bfast_r": {
"tag": ["time-series"],
- "url": "https://raw.githubusercontent.com/sepal-contrib/bfastspatial/master/www/tutorial/tutorial.rst"
- },
- "bootstrap_sampling_simulator": {
- "tag": ["inventories"]
+ "url": "https://raw.githubusercontent.com/sepal-contrib/bfastspatial/main/www/tutorial/tutorial.rst"
},
"clip-time-series": {
"description": "Generate a .pdf file containing time series clip on a given set of points",
@@ -46,7 +43,6 @@
"description": "Mapping all kind of canopy disturbances (natural or human induced) within (semi-)evergreen forests",
"url": "https://raw.githubusercontent.com/sepal-contrib/fcdm/release/doc/en.rst"
},
- "geo_processing": {},
"gfc_wrapper_python": {
"description": "Combine the GFC layers to produce a forest change map",
"url": "https://raw.githubusercontent.com/sepal-contrib/gfc_wrapper_python/release/doc/en.rst"
@@ -59,7 +55,6 @@
"tag": ["restoration"],
"url": "https://raw.githubusercontent.com/sepal-contrib/gwb/release/doc/en.rst"
},
- "gwl_analysis": {},
"vector_manager": {
"description": "Tool to manage vector files in SEPAL (upload, download, export to GEE and grid generation)",
"url": "https://raw.githubusercontent.com/sepal-contrib/vector_manager/release/doc/en.rst"
@@ -69,16 +64,9 @@
"tag": ["PlanetLab"],
"url": "https://raw.githubusercontent.com/sepal-contrib/planet-order/release/doc/en.rst"
},
- "plot_generator": {
- "tag": ["inventories"]
- },
"sae_analysis": {
"url": "https://raw.githubusercontent.com/openforis/accuracy-assessment/master/aa_analysis/doc.rst"
},
- "sae_design": {},
- "sar_time_series": {
- "tag": ["time-series"]
- },
"sdg_indicator": {
"description": "Monitor SDG indicators at plot level",
"tag": ["restoration"],
@@ -86,7 +74,8 @@
},
"sepafe": {
"tag": ["PlanetLab"],
- "url": "https://raw.githubusercontent.com/sepal-contrib/planet_active_fires_explorer/master/doc/en.rst"
+ "description": "Display active and past fire events from the Fire Information for Resource Management System (FIRMS) using Planet Lab data.",
+ "url": "https://raw.githubusercontent.com/sepal-contrib/planet_active_fires_explorer/release/doc/en.rst"
},
"sepal_mgci": {
"description": "Calculates the SDG 15.4.2: Mountain Green Cover Index at national/sub-regional scale",
@@ -113,10 +102,9 @@
"tag": ["SMFM"]
},
"tmf": {
- "description": "Wrapper for TMF"
- },
- "zonal-analysis": {
- "url": "https://raw.githubusercontent.com/sepal-contrib/zonal-analysis/release/doc/en.rst"
+ "description": "Tropical Moist Forest by EC-JRC",
+ "tag": ["other"],
+ "url": "https://raw.githubusercontent.com/sepal-contrib/tmf_sepal/release/doc/en.rst"
},
"gee-source": {
"description": "Extract source code from any GEE app URL",
@@ -128,6 +116,6 @@
},
"basin-river": {
"description": "Get Forest Cover Change by upstream sub-catchment",
- "url": "https://raw.githubusercontent.com/sepal-contrib/basin-rivers/master/doc/en.rst"
+ "url": "https://raw.githubusercontent.com/sepal-contrib/basin-rivers/release/doc/en.rst"
}
}
diff --git a/docs/source/_data/python_lib.txt b/docs/source/_data/python_lib.txt
index 3786db45c6..ce475aa909 100644
--- a/docs/source/_data/python_lib.txt
+++ b/docs/source/_data/python_lib.txt
@@ -5,10 +5,9 @@ scipy
shapely
shapely-geojson
tqdm
-xarray-leaflet
GDAL==$GDAL_VERSION
bqplot
-numpy==$NUMPY_VERSION
+numpy
geopandas
matplotlib
pandas
@@ -20,36 +19,33 @@ geeadd
######## Google Earthengine ########
oauth2client
google-api-python-client==1.12.8
-git+https://github.com/openforis/earthengine-api.git@v0.1.343#egg=earthengine-api&subdirectory=python
+git+https://github.com/openforis/earthengine-api.git@v0.1.374#egg=earthengine-api&subdirectory=python
oeel
######## BFAST dependencies ########
wget
-Sphinx==2.2.0
-sphinx-bootstrap-theme==0.7.1
+Sphinx
+sphinx-bootstrap-theme
numpydoc
git+https://github.com/12rambau/bfast.git
######## sepal modules ########
-geemap
Unidecode
pyperclip
python-dateutil
pytesmo
Wand
-PyPDF2 # more recent version are available (PyPDF4)
+PyPDF2 # more recent version are avaiable (PyPDF4)
rasterio
openpyxl
pre-commit
-ipywidgets==7.7.2
-ipyvuetify==1.8.2
######## web api ########
falcon
gunicorn
pyCrypto
-awscli
+awscli==1.11.18 # Pinned to prevent backtracking
######## other deps ########
xarray
@@ -58,8 +54,14 @@ dask-geopandas
nrt
seaborn
requests
-llvmlite
coverage
+geetools
+geeadd
+geeup
+cogee
+xee
+torch
+torchvision
######## OSK requirements ########
descartes
@@ -70,7 +72,7 @@ imageio
rtree
retrying
Cython
-pyproj==2.6.1 # Require proj update before 3.0.0 can be installed
+pyproj
######## Early Warning System for Canopy Disturbances in Ecuador (SATA) ########
nose
@@ -78,4 +80,5 @@ nosexcover
pylint
click
dateutils
-boto3
+boto3==1.4.3 # Pinned to prevent backtracking
+
diff --git a/docs/source/_data/r_packages.sh b/docs/source/_data/r_packages.sh
index 00983b107f..6810b15530 100644
--- a/docs/source/_data/r_packages.sh
+++ b/docs/source/_data/r_packages.sh
@@ -12,10 +12,6 @@ export JAVA_LD_LIBRARY_PATH=${JAVA_HOME}/lib/server:${JAVA_HOME}/lib
R CMD javareconf
-# Install CRAN packages via r-proxy
-
-R -e "install.packages('rgdal', version = '1.3-9', dependencies = TRUE, repos = 'http://r-proxy:8180/', upgrade = 'never')"
-
R -e "install.packages(c(\
'abind',\
'askpass',\
@@ -201,7 +197,6 @@ R -e "install.packages(c(\
'reshape2',\
'reticulate',\
'rgbif',\
- 'rgeos',\
'rgexf',\
'RgoogleMaps',\
'rhandsontable',\
@@ -295,11 +290,13 @@ R -e "install.packages(c(\
'yaml',\
'zeallot',\
'zoo'
- ), repos='http://r-proxy:8180/', upgrade = 'never')"
+ ), repos='http://r-proxy:8180/')"
-# Install GitHub packages via r-proxy
+# Install archived packages - this doesn't work through r-proxy
+R -e "install.packages('https://cran.r-project.org/src/contrib/Archive/rgdal/rgdal_1.6-7.tar.gz')"
+R -e "install.packages('https://cran.r-project.org/src/contrib/Archive/rgeos/rgeos_0.6-4.tar.gz')"
-R -e "install.packages('remotes', dependencies=TRUE, repos='http://r-proxy:8180/', upgrade = 'never')"
+R -e "install.packages('remotes', dependencies=TRUE, repos='http://r-proxy:8180/')"
R -e "remotes::install_url(c(\
'http://r-proxy:8180/github/r-barnes/dggridR/archive/refs/heads/master.tar.gz',\
@@ -307,5 +304,5 @@ R -e "remotes::install_url(c(\
'http://r-proxy:8180/github/azvoleff/gfcanalysis/archive/refs/heads/master.tar.gz',\
'http://r-proxy:8180/github/loicdtx/bfastSpatial/archive/refs/heads/master.tar.gz',\
'http://r-proxy:8180/github/jreiche/bayts/archive/refs/heads/master.tar.gz',\
- 'http://r-proxy:8180/github/cran/gdalUtils/archive/refs/heads/master.tar.gz'
- ), repos='http://r-proxy:8180/', build = FALSE, upgrade = 'never')"
+ 'http://r-proxy:8180/github/cran/gdalUtils/archive/refs/heads/master.tar.gz'\
+ ), repos='http://r-proxy:8180/', build = FALSE)"
diff --git a/docs/source/_script/lint_trailing.py b/docs/source/_script/lint_trailing.py
new file mode 100644
index 0000000000..56ccb4503b
--- /dev/null
+++ b/docs/source/_script/lint_trailing.py
@@ -0,0 +1,45 @@
+import os
+import sys
+
+from docutils import ApplicationError
+
+
+def process_file(file_path):
+ """Process a single .rst file, checking for validity and fixing whitespace."""
+ try:
+ with open(file_path, "r") as f:
+ content = f.read()
+ # publish_string(source=content, writer_name="null") # Validate RST
+ with open(file_path, "w") as f:
+ f.writelines([line.rstrip() + "\n" for line in content.splitlines()])
+ print("done")
+ except (IOError, ApplicationError) as e:
+ print(f"Skipping file {file_path}: {e}")
+
+
+def process_directory(path):
+ """Process all .rst files in a directory."""
+ for root, dirs, files in os.walk(path):
+ for file in files:
+ if file.endswith(".rst"):
+ process_file(os.path.join(root, file))
+
+
+def main():
+ """Main function for the script."""
+ if len(sys.argv) != 2:
+ print(f"Usage: {sys.argv[0]} path_or_file")
+ sys.exit(1)
+
+ path_or_file = sys.argv[1]
+ if os.path.exists(path_or_file):
+ if os.path.isdir(path_or_file):
+ process_directory(path_or_file)
+ else:
+ process_file(path_or_file)
+ else:
+ print(f"The path {path_or_file} does not exist.")
+
+
+if __name__ == "__main__":
+ main()
diff --git a/docs/source/_script/modules.py b/docs/source/_script/modules.py
index 3b9df7102c..c3d6f3bb78 100755
--- a/docs/source/_script/modules.py
+++ b/docs/source/_script/modules.py
@@ -44,7 +44,6 @@ def get_modules():
module_list = json.loads(module_json.read_text())
for name in module_list:
-
dst = dwn_dir / f"{name}.rst"
file = module_list[name].get("url", no_module_url)
@@ -60,9 +59,10 @@ def get_modules():
if file == no_module_url:
txt = txt.replace("Module_name", name).replace("=", "=" * len(name))
- # add the custom edit directive to the file to ensure the "edit this page"
- # point to the correct file.
- txt += f"\n.. custom-edit:: {file}\n"
+ if file != no_module_url:
+ # add the custom edit directive to the file to ensure the "edit this page"
+ # point to the correct file.
+ txt += f"\n\n.. custom-edit:: {file}\n"
dst.write_text(txt)
@@ -83,7 +83,6 @@ def get_tags():
tags = list(dict.fromkeys(tags))
for tag in tags:
-
# create a stub file
tag_file = module_dir / f"{tag}.rst"
copy(tag_template, tag_file)
@@ -130,7 +129,6 @@ def get_translation():
# loop in the modules
for name in module_list:
-
locale_folder = module_list[name].get("locale")
doc_url = module_list[name].get("url")
diff --git a/docs/source/_static/css/custom.css b/docs/source/_static/css/custom.css
index cc0bf2f860..1bd863224a 100644
--- a/docs/source/_static/css/custom.css
+++ b/docs/source/_static/css/custom.css
@@ -94,43 +94,6 @@ html[data-theme="dark"] img:not(.only-dark):not(.dark-light) {
filter: none;
}
-/*******************************************************************************
-* characteristic of the map
-*
-**/
-#map,
-#map-overlay {
- position: fixed;
- top: 0;
- left: 0;
- margin: 0;
- padding: 0;
- height: 100vh;
- width: 100vw;
-}
-
-#map {
- z-index: -2;
-}
-#map-overlay {
- z-index: -1;
-}
-html[data-theme="light"] #map-overlay {
- background-color: rgba(255, 255, 255, 0.8);
-}
-html[data-theme="dark"] #map-overlay {
- background-color: rgba(18, 18, 18, 0.3);
-}
-.leaflet-container {
- background: var(--pst-color-background) !important;
-}
-.lb-outerContainer {
- background-color: var(--pst-color-background);
-}
-footer {
- background-color: var(--pst-color-background);
-}
-
/*******************************************************************************
* custom article footer rendering
*/
diff --git a/docs/source/_templates/community.html b/docs/source/_templates/community.html
index d7aae64db4..606013e8cb 100644
--- a/docs/source/_templates/community.html
+++ b/docs/source/_templates/community.html
@@ -1,6 +1,8 @@
diff --git a/docs/source/_templates/disclaimer.rst b/docs/source/_templates/disclaimer.rst
index e826c272e2..80a900a37c 100644
--- a/docs/source/_templates/disclaimer.rst
+++ b/docs/source/_templates/disclaimer.rst
@@ -1,3 +1,3 @@
-.. danger::
+.. attention::
- This section is intended for the Sepal team members only. If you ended up here there is no need to read this documentation, You'll not have access to the referred tools
+ The **SEPAL team documentation** section was developed for SEPAL team members only. While contributors and collaborators are encouraged to review these articles, they are not mandatory for general SEPAL users.
diff --git a/docs/source/_templates/e-learning.html b/docs/source/_templates/e-learning.html
index 316a8b23cc..c1c881cabe 100644
--- a/docs/source/_templates/e-learning.html
+++ b/docs/source/_templates/e-learning.html
@@ -1,6 +1,6 @@
diff --git a/docs/source/_templates/index.rst b/docs/source/_templates/index.rst
index 1a44a55bfc..e588e88bf2 100644
--- a/docs/source/_templates/index.rst
+++ b/docs/source/_templates/index.rst
@@ -1,13 +1,15 @@
-SEPAL applications
-==================
+Modules
+=======
*Access complex workflows without the need of digital programming skills with SEPAL applications*
-.. custom-edit:: https://raw.githubusercontent.com/openforis/sepal-doc/main/docs/source/_templates/index.rst
-
Overview
--------
-SEPAL applications are tools provided by the SEPAL team from the Food and Agriculture Organization of the United Nations (FAO) or other developers. These tools are based on advanced geographic information system (GIS) libraries and complement the functionalities of SEPAL recipes, providing the end user with access to complex workflows without requiring knowledge of digital programming. These applications are served in SEPAL with attractive user interfaces to ensure the best possible user experience.
+SEPAL applications are tools provided by the SEPAL team from the Food and Agriculture Organization of the United Nations (FAO) or other developers.
+
+These tools are based on advanced geographic information system (GIS) libraries and complement the functionalities of SEPAL recipes, providing the end user with access to complex workflows without requiring knowledge of digital programming.
+
+The applications are served in SEPAL with attractive user interfaces to ensure the best possible user experience.
They run on various shells – from pure HTML and Shiny-based R apps to Jupyter-powered Python kernels. This flexibility and variety of tools allows developers to integrate workflows and make them available for SEPAL users.
@@ -16,22 +18,22 @@ Review the following subsections to learn more about SEPAL applications.
Start applications
------------------
-To start a SEPAL application, go to the **Apps** dashboard by selecting the wrench icon on the left side of the window (see **1** in the following image). This will display a list of apps you can run in the interface. More information about each app is found by selecting the **i** on the right side.
+To start a SEPAL application, go to the **Apps** dashboard by selecting the wrench icon on the left side of the window (see **1** in the following image). This will display a list of apps you can run in the interface. More information about each app is found by selecting the **More information** (**i**) icon on the right side.
-Note that the following apps are code editors, not apps (**2**):
+Note that the following are code editors, not apps (**2**):
- **RStudio**: Provides access to the R environment where you can run processing scripts and upload data to your SEPAL folder.
- **Jupyter Notebook**: An open-source web application that allows you to create and share documents that contain live code, equations, visualizations and narrative text (supports kernels in many different languages, including R, Python and NodesJS).
- **JupyterLab**: A web-based interactive development environment for Jupyter notebooks, code and data.
.. thumbnail:: ../_images/module/index/dashboard.png
- :title: The landing page of the **Apps** dashboard
+ :title: The landing page of the Apps dashboard
To find the application you're looking for, navigate through the different app pages (**3**), use tags (**4**), or utilize the search bar (**5**).
Once the desired application is found, select it to initiate the process. This will start the smallest instance to run the SEPAL sandbox (:code:`t1`).
-Refer to the next section to start a specific instance manually.
+Refer to the next subsection to start a specific instance manually.
.. note::
@@ -62,7 +64,7 @@ If a particular app's documentation requires the user to start a specific instan
3. Wait for the instance to finish loading.
.. thumbnail:: ../_images/module/index/m4_started.png
- :title: Start an **m4** instance
+ :title: Start an m4 instance
4. Go back to the dashboard of the application to launch your app. It will automatically use the instance you opened and won't restart a :code:`t1`.
diff --git a/docs/source/_templates/issue-tracker.html b/docs/source/_templates/issue-tracker.html
index 3c5ba39bda..6d709c1ba1 100644
--- a/docs/source/_templates/issue-tracker.html
+++ b/docs/source/_templates/issue-tracker.html
@@ -1,6 +1,8 @@
diff --git a/docs/source/_templates/module_tag.rst b/docs/source/_templates/module_tag.rst
index 13e00ff174..192c54a740 100644
--- a/docs/source/_templates/module_tag.rst
+++ b/docs/source/_templates/module_tag.rst
@@ -1,7 +1,7 @@
module_tag
=
-List of the modules gathered under the module_tag tag:
+Modules with the **module_tag** tag include:
.. toctree::
:maxdepth: 1
diff --git a/docs/source/_templates/no_feature.rst b/docs/source/_templates/no_feature.rst
index 45a2cf86c0..e859e4173f 100644
--- a/docs/source/_templates/no_feature.rst
+++ b/docs/source/_templates/no_feature.rst
@@ -1,7 +1,3 @@
-.. warning::
+.. note::
- The documentation of this feature is under construction.
-
-.. tip::
-
- For specific help, please open an issue on our repository by clicking on this `link `__.
+ This article of SEPAL documentation is not yet available. To request that it be made available, use the `GitHub Issue Tracker `__.
diff --git a/docs/source/_templates/no_module.rst b/docs/source/_templates/no_module.rst
index dcb09f47f4..974cf9eab8 100644
--- a/docs/source/_templates/no_module.rst
+++ b/docs/source/_templates/no_module.rst
@@ -1,10 +1,6 @@
Module_name
=
-.. warning::
+.. note::
- The english documentation of the module have is under construction.
-
-.. tip::
-
- For specific help, please open an issue on our repository by clicking on this `link `__.
+ This article of SEPAL documentation is not yet available. To request that it be made available, use the `GitHub Issue Tracker `__.
diff --git a/docs/source/_templates/no_recipe.rst b/docs/source/_templates/no_recipe.rst
index 6e50b38436..57755a89a6 100644
--- a/docs/source/_templates/no_recipe.rst
+++ b/docs/source/_templates/no_recipe.rst
@@ -1,7 +1,3 @@
-.. warning::
+.. note::
- The documentation of this recipe is under construction.
-
-.. tip::
-
- For specific help, please open an issue on our repository by clicking on this `link `__.
+ This article of SEPAL documentation is not yet available. To request that it be made available, use the `GitHub Issue Tracker `_.
diff --git a/docs/source/_templates/stackexchange.html b/docs/source/_templates/stackexchange.html
index 5acd4447e1..110b91bf5c 100644
--- a/docs/source/_templates/stackexchange.html
+++ b/docs/source/_templates/stackexchange.html
@@ -1,6 +1,6 @@
diff --git a/docs/source/cli/index.rst b/docs/source/cli/index.rst
index 5a0ef5b17b..b84ba814b6 100644
--- a/docs/source/cli/index.rst
+++ b/docs/source/cli/index.rst
@@ -1,18 +1,23 @@
-CLI utilities and coding tools
-==============================
-*Use command-line interface (CLI) utilities and coding tools in SEPAL*
+CLI
+===
+*Use CLI utilities and coding tools in SEPAL*
CLI tools
---------
-The SEPAL platform includes a variety of useful command-line interface (CLI) utilities to help resolve specific problems, such as:
+To help resolve specific problems, the SEPAL platform includes a variety of useful command-line interface (CLI) utilities, including:
-- **Geospatial Data Abstraction Library (GDAL)**
-- **Google Drive (Drive)**
-- **Google Earth Engine (GEE)**
-- **GuidosToolbox Workbench (GWB)**
-- **Open Foris Geospatial Toolkit (OFGT)**
-- **Orfeo Toolbox (OTB)**
+.. toctree::
+ :maxdepth: 1
+
+ gdal
+ gdrive
+ gee
+ gwb
+ ofgt
+ otb
+ python
+ r
These tools can be called directly from the terminal or via any programming language sending commands to the kernel, including R and Python (installed by default on any SEPAL account).
@@ -21,7 +26,7 @@ These tools can be called directly from the terminal or via any programming lang
.. note::
- The code executed previously on an existing :code:`example.tif` file.
+ The code executed previously on an existing :code:`example.tif` file:
.. code-block:: console
@@ -33,13 +38,13 @@ These tools can be called directly from the terminal or via any programming lang
.. tip::
- If the code you want to execute is taking time, consider running it in the background using :code:`nohup`.
+ If the code you want to execute is taking time, consider running it in the background using :code:`nohup`:
.. code-block:: console
nohup gdalinfo example.tif
- All console outputs will be redirected to a :code:`nohup.out` in your home directory, but the execution will be running in the background. Thus, you will be able to safely close the terminal or even the browser window without killing your process (for more information about :code:`nohup`, see `this article `__.
+ All console outputs will be redirected to a :code:`nohup.out` in your home directory, but the execution will be running in the background. You will be able to safely close the terminal or even the browser window without interrupting the process (for more information about :code:`nohup`, see `this article `__).
Coding tools
------------
@@ -47,26 +52,13 @@ Coding tools
In the **Apps** section, there are three coding tools at the top of the list:
- JupyterLab
-- JupyterNotebook
+- Jupyter Notebook
- RStudio
.. thumbnail:: ../_images/cli/index/code_editor.png
:title: The three code editors in the SEPAL environment
-They will allow the user to code wokflows in any of the available languages using the corresponding environment in SEPAL. These environments are fully customizable (select the :code:`Python` or :code:`R` section to know more).
+They will allow the user to code wokflows in any of the available languages using the corresponding environment in SEPAL. These environments are fully customizable (view the :code:`Python` or :code:`R` section to know more).
.. thumbnail:: ../_images/cli/index/jupyter_example.png
:title: Example of **rasterio** code running in SEPAL JupyterLab (code extracted from **rasterio** documentation: https://rasterio.readthedocs.io/en/latest/topics/plotting.html)
-
-.. toctree::
- :hidden:
- :maxdepth: 1
-
- gdal
- gdrive
- gee
- gwb
- ofgt
- otb
- python
- r
diff --git a/docs/source/cli/ofgt.rst b/docs/source/cli/ofgt.rst
index eb32ff5980..2894af45be 100644
--- a/docs/source/cli/ofgt.rst
+++ b/docs/source/cli/ofgt.rst
@@ -3,7 +3,7 @@ OFGT
The **Open Foris Geospatial Toolkit (OFGT)** is a collection of prototype command-line utilities for processing geographical data. The tools can be divided into stand-alone programmes and scripts. They have been tested mainly in the Ubuntu Linux environment, although they can be used with other Linux distros, macOS, and Microsoft Windows (Cywgin) as well. Most of the stand-alone programmes use **GDAL libraries** and many of the scripts rely heavily on **GDAL command-line utilities**.
-The **OFGT** project started under the Open Foris initiative in an effort to develop, share and support software tools and methods for multipurpose forest assessment, monitoring and reporting (see `Open Foris `__.
+The **OFGT** project started under the Open Foris initiative in an effort to develop, share and support software tools and methods for multipurpose forest assessment, monitoring and reporting (see `Open Foris `__).
The Open Foris initiative develops and supports innovative, easy-to-use tools needed to produce reliable, up-to-date information on the state of forest resources and their uses.
diff --git a/docs/source/cookbook/ccdc.rst b/docs/source/cookbook/ccdc.rst
index f46170e72a..c17ff33775 100644
--- a/docs/source/cookbook/ccdc.rst
+++ b/docs/source/cookbook/ccdc.rst
@@ -1,6 +1,6 @@
CCDC asset creation
===================
-*Facilitate the workflow for applying the Continuous Change Detection and Classification approach with SEPAL*
+*Facilitate the workflow for applying the CCDC approach with SEPAL*
Background
diff --git a/docs/source/cookbook/classification.rst b/docs/source/cookbook/classification.rst
index 1007595c73..089bf93675 100644
--- a/docs/source/cookbook/classification.rst
+++ b/docs/source/cookbook/classification.rst
@@ -346,7 +346,7 @@ The following table is compatible with SEPAL:
32.77189961605467,-11.616264558754402,80,Shrublands,Pierrick rambaud
...
-The columns used to define the X (longitude) and Y (latitude) coordinates are manually set up in the pop-up window. Select :btn:` Next` once every column is filled.
+The columns used to define the X (longitude) and Y (latitude) coordinates are manually set up in the pop-up window. Select :btn:` Next` once every column is filled.
.. thumbnail:: ../_images/cookbook/classification/import-training-csv-coords.png
:group: classification-recipe
@@ -366,7 +366,7 @@ Now that you have set up the coordinates of your points, SEPAL will request the
:group: classification-recipe
:title: Import a .csv file in SEPAL as training data.
-Select :btn:` next` to add the data to the model. SEPAL will provide a summary of the classes in the legend of the classification and the number of training points added by your file.
+Select :btn:` next` to add the data to the model. SEPAL will provide a summary of the classes in the legend of the classification and the number of training points added by your file.
Selecting the :btn:` Done` button will complete the uploading procedure.
diff --git a/docs/source/cookbook/index.rst b/docs/source/cookbook/index.rst
index 603cc97ca6..2158a14ced 100644
--- a/docs/source/cookbook/index.rst
+++ b/docs/source/cookbook/index.rst
@@ -1,13 +1,13 @@
-SEPAL recipes
-=============
-*Harness high-performance cloud-based computing and modern geospatial data infrastructures with SEPAL recipes*
+Cookbook
+========
+*Harness high-performance cloud-based computing and modern geospatial data infrastructures with recipes from the SEPAL cookbook*
Overview
--------
SEPAL recipes are the main feature of the platform, offering users the ability to quickly and efficiently query and process satellite data, tailor their products for local needs, and produce sophisticated and relevant geospatial analyses.
-A SEPAL recipe is a record of the steps and parameters used to make a dataset (e.g. optical mosaic, radar mosaic, classification). The recipe can be saved, with the same data recreated on-the-fly whenever needed or used in further analyses. A recipe is not, in itself, data. Using SEPAL recipes enables documentation of the parameters and steps used to create mosaics, composites, classifications, time series and other datasets or information products. SEPAL recipes, once saved, are available in the SEPAL interface after you sign in to the platform. Recipes can be run, deleted or copied (e.g. to change the sensor used, while leaving all other parameters the same).
+A SEPAL recipe is a record of the steps and parameters used to make a dataset (e.g. optical mosaic, radar mosaic, classification). The recipe can be saved, with the same data recreated on the fly whenever needed or used in further analyses. A recipe is not, in itself, data. Using SEPAL recipes enables documentation of the parameters and steps used to create mosaics, composites, classifications, time series and other datasets or information products. SEPAL recipes, once saved, are available in the SEPAL interface after you sign in to the platform. Recipes can be run, deleted or copied (e.g. to change the sensor used, while leaving all other parameters the same).
With recipes, you can access the Google Earth Engine (GEE) multi-petabyte catalog of satellite imagery and utilize their planetary-scale analysis capabilities without writing a single line of code, simply by linking your Google and SEPAL accounts.
@@ -15,6 +15,22 @@ With recipes, you can access the Google Earth Engine (GEE) multi-petabyte catalo
You cannot export a recipe as an asset or a :code:`.tiff` file without a small computation quota. If you are a new user, see :doc:`../setup/resource`.
+Recipes
+-------
+Recipes in the SEPAL cookbook include:
+
+.. toctree::
+ :maxdepth: 1
+
+ optical_mosaic
+ radar_mosaic
+ planet_mosaic
+ classification
+ time_series
+ ccdc
+ ccdc_slice
+ class_change
+
Gallery
-------
@@ -75,7 +91,7 @@ Start a recipe
Connect your SEPAL account to your GEE account to read and write GEE assets. If your accounts are not linked, you will only be able to download data to your SEPAL workspace.
-To start a recipe, go to the **Process** tab :icon:`fa-solid fa-globe`, where you'll see the list of all saved recipes in your SEPAL account.
+To start a recipe, go to the **Process** tab (:icon:`fa-solid fa-globe`), where you'll see the list of all saved recipes in your SEPAL account.
.. thumbnail:: https://user-images.githubusercontent.com/149204/132474880-12333a36-dee0-4bdc-a0b4-0e9aab24b601.png
:title: Recipe list displayed in the interface
@@ -100,18 +116,3 @@ The file will be downloaded to your computer using the following name: :code:``__.
-Radar for Detecting Deforestation (RADD)
-########################################
+RADD
+####
.. note::
- RADD alerts only cover the tropical part of Africa and the Americas (for more information, see their documenation).
+ Radar for Detecting Deforestation (RADD) alerts only cover the tropical part of Africa and the Americas (for more information, see their documenation).
By selecting this alert system, you will use RADD alerts.
@@ -130,14 +132,14 @@ By selecting this alert system, you will use RADD alerts.
More information on these alerts can be found on the `Wageningen University portal `__.
-Near real-time (NRT)
-####################
+NRT
+###
.. attention::
- This functionality will remain experimental until the SEPAL team removes the **Beta** status on the near real-time alert creation recipe.
+ This functionality will remain experimental until the SEPAL team removes the **Beta** status on the **NRT alert creation** recipe.
-By selecting this alert system, users will use near real-time alerts provided by the SEPAL recipe on a specific AOI for specific dates. You only need to provide access to the alert asset.
+By selecting this alert system, users will use NRT alerts provided by the SEPAL recipe on a specific AOI for specific dates. You only need to provide access to the alert asset.
.. note::
@@ -148,11 +150,11 @@ GLAD-S
.. attention::
- At the time of writing this article (26 April 2022), only northern regions of South America were covered by the alert system. To see the area in the GEE code editor, go to `this link `__ .
+ At the time of writing this article (26 April 2022), only northern regions of South America were covered by the alert system. To see the area in the GEE code editor, see `this page `__ .
-By selecting this alert system, you will use GLAD alerts based on Sentinel-2 satellites.
+By selecting this alert system, you will use GLAD alerts based on Sentinel-2 satellites (GLAD-S).
- Loss of primary forest is mapped in near real-time at a 10 metre resolution using Sentinel-2 multispectral data. Cloud, shadow and water are detected in each new Sentinel-2 image and a forest loss algorithm is applied to all remaining clear land observations. The algorithm relies on the spectral data in each new image, in combination with spectral metrics from a baseline period of the previous two years. Confidence is built through repeated loss observations in the consequent images.
+ Loss of primary forest is mapped in NRT at a 10 m resolution using Sentinel-2 multispectral data. Cloud, shadow and water are detected in each new Sentinel-2 image and a forest loss algorithm is applied to all remaining clear land observations. The algorithm relies on the spectral data in each new image, in combination with spectral metrics from a baseline period of the previous two years. Confidence is built through repeated loss observations in the consequent images.
CUSUM
#####
@@ -161,7 +163,7 @@ CUSUM
This will use the :code:`.tif` output of :doc:`cusum`.
-Once you've run the CUSUM module, you'll obtain a three-band :code:`.tif` file. Ingest this file in GEE using the `code editor interface `__. Once the map is available in your assets, you can use it in the module. If you don't find the asset in the list, select the :icon:`fa-solid fa-arrows-rotate` icon to reload your asset list.
+Once you've run the **CUSUM** module, you'll obtain a three-band :code:`.tif` file (CUSUM refers to cumulative sum). Ingest this file in GEE using the `code editor interface `__. Once the map is available in your assets, you can use it in the module. If you don't find the asset in the list, select the :icon:`fa-solid fa-arrows-rotate` icon to reload your asset list.
.. note::
@@ -199,7 +201,7 @@ The source needs to be a GeoJSON file using the following format:
.. note::
- The Vietnamese Forest Department is using a specific alert system that works well. Developed in partnership with JICA, the system generates a GEOjson file every ten days. To see the GEE application, go to `this link `__ (note: content is only available in Vietnamese).
+ The Vietnamese Forest Department is using a specific alert system that works well. Developed in partnership with Japan International Cooperation Agency (JICA), the system generates a GEOjson file every ten days. To see the GEE application, see `this page `__ (note: content is only available in Vietnamese).
RECOVER
#######
@@ -211,11 +213,11 @@ Save your work by exporting the already interpreted alerts in :code:`.gpkg` form
JJ-FAST
#######
-By selecting this alert system, you will use the JJ-FAST alerts based on ALOS PALSAR data.
+By selecting this alert system, you will use JJ-FAST alerts based on ALOS PALSAR data (JJ-FAST refers to the JICA-JAXA Forest Early Warning System in the Tropics; JAXA refers to Japan Aerospace Exploration Agency).
- The JICA-JAXA Forest Early Warning System in the Tropics (JJ-FAST) can detect deforestation sites with sizes larger than 2 hectares (Version 3.0, as of June 2020). Employing microwave remote sensing technology, detections can be made even under thick cloud cover, which is characteristic for tropical regions, especially during rainy seasons. The system detects deforestation by means of L-band (1.25 MHz) Synthetic Aperture Radar (SAR) data acquired by the PALSAR-2 sensor aboard JAXA’s Advanced Land Observing Satellite 2 (ALOS-2) and provides the positioning information of detected sites to users free of charge via its web service.
+ JJ-FAST can detect deforestation sites with sizes larger than 2 ha (Version 3.0, as of June 2020). Employing microwave remote sensing technology, detections can be made even under thick cloud cover, which is characteristic for tropical regions, especially during rainy seasons. The system detects deforestation by means of L-band (1.25 MHz) Synthetic Aperture Radar (SAR) data acquired by the PALSAR-2 sensor aboard JAXA’s Advanced Land Observing Satellite 2 (ALOS-2) and provides the positioning information of detected sites to users free of charge via its web service.
- With frequent updates for the entire tropical forest belt, approximately every one and a half months, JJ-FAST aims to function as an effective deterrent against illegal deforestation activities when it is utilized for forest monitoring in target countries.
+ With frequent updates for the entire tropical forest belt – approximately every one and a half months – JJ-FAST aims to function as an effective deterrent against illegal deforestation activities when it is utilized for forest monitoring in target countries.
Government forest authorities of tropical countries with large forest inventories are expected to be the main users of JJ-FAST. Since polygons of detected deforestation cannot only be conveniently viewed online, but also downloaded for further geographic information system (GIS) analysis, local authorities are able to effectively identify illegal activities by comparing JJ-FAST detections with available national land use maps and/or concession maps.
@@ -231,17 +233,17 @@ Once everything is set, select :btn:`select alerts` and the module will start do
Metadata
--------
-Select :btn:`` to show the metadata panel, which allows you to validate the alerts identified by the driver using Planet VHR (very high-resolution) imagery. All information about the current alert will be displayed in this table:
+Select :btn:`` to show the **Metadata** pane, which allows you to validate alerts identified by the driver using Planet VHR (very high-resolution) imagery. The following information about the current alert will be displayed:
-- Alert ID: the ID of the alert
-- Geometry edition: a button to trigger geometry edition for one single alert
-- Date: the identified date of the deforestation event
-- Surface: the deforested surface in hectares
-- Coordinates: the coordinates of the centre of the alert
-- Review: the visual evaluation performed by the user
-- Comments: additional comments on the alert
+- **Alert ID**: the ID of the alert
+- **Geometry edition**: a button to trigger geometry edition for one single alert
+- **Date**: the identified date of the deforestation event
+- **Surface**: the deforested surface in hectares
+- **Coordinates**: the coordinates of the centre of the alert
+- **Review**: the visual evaluation performed by the user
+- **Comments**: additional comments on the alert
-The following sections will cover the editable fields of this table.
+The following subsections of this article will cover the editable fields in the **Metadata** pane.
.. thumbnail:: https://raw.githubusercontent.com/sepal-contrib/alert_module/master/doc/img/metadata.png
:title: The metadata of the alerts
@@ -250,7 +252,7 @@ The following sections will cover the editable fields of this table.
Alert ID
^^^^^^^^
-In the upper section, the list of alerts are ordered by size. To access them, use the blue arrows or select the caret to select one in the dropdown menu. Once an alert is selected (represented now in orange on the map), the **Planet** panel will open itself in the upper-right corner of the map and the information associated with the alert will be displayed.
+In the upper section, the list of alerts are ordered by size. To access them, use the blue arrows or select the caret to select one in the dropdown menu. Once an alert is selected (represented now in orange on the map), the **Planet** pane will open itself in the upper-right corner of the map and the information associated with the alert will be displayed.
.. tip::
@@ -265,7 +267,7 @@ Geometry edition
Some drivers perform automatic analysis; sometimes the geometry of the alerts poorly represent what you see in the VHR imagery. By using this module, you can redefine the geometry before exporting your results to perfectly fit the deforested area.
-- Select :btn:`edit geometry` to open the edition interface (1).
+- Select :btn:`edit geometry` to open the **Editing** interface.
- Select :btn:`` to start editing; move the white square to add or remove vertices.
- To finish, select :btn:`save` to exit editing mode.
@@ -282,19 +284,19 @@ Alternatively:
:title: The reset process to cancel edits
:group: alert-module
-Once editing is complete, select the :btn:`finish edition` button in the **Metadata** panel.
+Once editing is complete, select the :btn:`finish edition` button in the **Metadata** pane.
Date
^^^^
-If the selected driver embeds the dates of the alerts, this field will be already filled with a meaningful date of a deforestation event; if it does not, use the date found in the file title.
+If the selected driver embeds the dates of the alerts, this field will be already completed with a meaningful date of a deforestation event; if it does not, use the date found in the file title.
Once the deforestation event is identified, update the date value to reflect what you see in the VHR imagery. Click in the field to use the date selector.
Review
^^^^^^
-By default, all alerts are set to :code:`unset`. After interpreting Planet imagery, change the value of the radio "review" from:
+By default, all alerts are set to :code:`unset`. After interpreting Planet imagery, change the value of the radio review to:
- :code:`yes`: the alert is valid, as well as the date
- :code:`no`: the alert is not valid (i.e. no deforestation event)
@@ -308,27 +310,27 @@ You can fill out this comment section with any aditional information. There are
Export
^^^^^^
-In the lower portion of the **Metadata** panel, there are three exportation buttons; each one will export the alerts and their reviews in a specific format.
+In the lower portion of the **Metadata** pane, there are three exportation buttons; each one will export the alerts and their reviews in a specific format.
-to .kml
-#######
+Export as .kml
+##############
Export alerts as a :code:`.kml` file, readable with Google Earth. Each alert will use its ID as the label. You can export them at the beginning of the review if you want to use Google Earth in the review process.
-to .gpkg
-########
+Export as .gpkg
+###############
-Export alerts as a :code:`.gpkg` file, readable by any GIS software. It will embed the geometry and all the properties associated with each feature/alert (including the original geometry). This file can be used to save progress and reused as an input of the process.
+Export alerts as a :code:`.gpkg` file, readable by any GIS software. It will embed the geometry and all properties associated with each feature/alert (including the original geometry). This file can be used to save progress and reused as an input of the process.
-to .csv
-#######
+Export as .csv
+##############
Export alerts as a :code:`.csv` file. The properties of each alert are kept; the file represents each feature using the coordinates (latitude/longitude) of the centre of each alert.
Planet imagery
--------------
-To interprete the validity of the alert, this module is based on Planet NICFI imagery.
+To interprete the validity of the alert, this module is based on NICFI – Planet imagery.
Parameters
^^^^^^^^^^
@@ -337,9 +339,9 @@ Parameters
This is optional. If nothing is set, the module will use Planet NICFI Level 1 data (monthly mosaics). If you have NICFI Level 2 access, providing your API key will grant you access to daily imagery.
-Select :btn:`` to access the **Planet API** interface. In this panel, you can connect to your Planet profile using your credentials or your password.
+Select :btn:`` to access the **Planet API** interface. In this pane, you can connect to your Planet profile using your credentials or your password.
-- Select credential mode between "credentials" and "API key"
+- Select credential mode (between **Credentials" and **API key**)
- Set and validate your credentials
If the icon is green, you are connected.
@@ -390,7 +392,7 @@ Select :btn:`` to change the color of the images from CIR t
Level 2 (daily)
^^^^^^^^^^^^^^^
-.. attention::
+.. note::
This option is only available for users that have NICFI Level 2 access.
@@ -398,9 +400,9 @@ Level 2 data are daily imagery. When an alert is selected, the module will load
.. tip::
- Since multiple images are displayed at once, don't hesitate to play with the layer control to hide and show different scenes.
+ Since multiple images are displayed at once, don't hesitate to play with the **Layer** control to hide and show different scenes.
-Navigate through the images using the buttons in the **Planet navigator**. Use :btn:`` and :btn:`` to move one day at a time into the past or future. Use :btn:`` and :btn:`` to move one month at a time into the past or future). The :btn:`` button will set the closest date from the observation date.
+Navigate through the images using the buttons in the **Planet navigator**. Use :btn:`` and :btn:`` to move one day at a time into the past or future. Use :btn:`` and :btn:`` to move one month at a time into the past or future). The :btn:`` button will set the closest date from the observation date.
.. thumbnail:: https://raw.githubusercontent.com/sepal-contrib/alert_module/master/doc/img/planet_daily.png
:title: Planet daily mosaic displayed in CIR
diff --git a/docs/source/modules/dwn/alos_mosaics.rst b/docs/source/modules/dwn/alos_mosaics.rst
index 6a22197f59..555b79048c 100644
--- a/docs/source/modules/dwn/alos_mosaics.rst
+++ b/docs/source/modules/dwn/alos_mosaics.rst
@@ -1,9 +1,15 @@
-ALOS Kyoto & Carbon Mosaics by JAXA
-===================================
+ALOS K&C mosaics
+================
+*Create and manipulate ALOS K&C mosaics*
-This module was adapted to the SEPAL environment from `this script by Vollrath `_.
-Designed on top of the interactive framework `sepal_ui `_, this module allows users to extract ALOS K&C mosaics.
+This module allows users to:
+
+- extract ALOS Kyoto & Carbon (ALOS K&C) mosaics by JAXA;
+- display the mosaics in interactive maps; and
+- export the mosaics as Google Earth Engine (GEE) assets and/or SEPAL .tif files.
+
+Designed on top of the interactive framework `sepal_ui `_, it was adapted to the SEPAL environment from `this script by Vollrath `_.
Necessary inputs include:
@@ -11,9 +17,7 @@ Necessary inputs include:
- a year
- select filters
-The user will be able to display the mosaic in an interactive map and export it as a Google Earth Engine (GEE) asset and/or SEPAL .tif file.
-
-The three-step process is described in the sections below, as well as presented in the following video tutorial.
+The three-step process is described in the subsections below, as well as presented in the following video tutorial.
.. youtube:: Asc8Nz0B1DI
@@ -28,7 +32,7 @@ Using the provided **AOI selector**, select an AOI of your choice between the di
.. note::
- If a custom AOI from a shape or drawing is selected, you will be able to use it directly. The upload to GEE will be launched in the background.
+ If a custom AOI from a shape or drawing is selected, you will be able to use it directly (the upload to GEE will be launched in the background).
Process mosaic
--------------
@@ -43,7 +47,7 @@ In the **Process** tile, set the different parameters of your mosaic:
- **Shadow masking**: activate or deactivate shadow masking
- **Db**: whether or not to scale the output to Db
-After setting your parameters, select the button. The dataset will be automatically sent to the **Vizualization** tile.
+After setting your parameters, select the button (the dataset will be automatically sent to the **Vizualization** tile).
.. figure:: https://raw.githubusercontent.com/lecrabe/alos_mosaics/main/doc/img/parameters.png
@@ -67,7 +71,10 @@ Choose from three diplay options:
Export dataset
--------------
-When you're satified with the information displayed, it can be exported for further use in GIS software or in a GEE process. The tool provides two main exportation options: as an asset (in GEE) or a .tif file (in SEPAL).
+When you're satified with the information displayed, it can be exported for further use in GIS software or in a GEE process. The tool provides two main exportation options:
+
+- an asset (in GEE), or
+- a .tif file (in SEPAL).
Both use the GEE export system and share the same set of parameters:
@@ -80,7 +87,7 @@ Both use the GEE export system and share the same set of parameters:
.. note::
- The default export parameters include: 25 metre resolution with backscatter and RFDI.
+ The default export parameters include: 25 m resolution with backscatter and RFDI.
.. figure:: https://raw.githubusercontent.com/lecrabe/alos_mosaics/main/doc/img/export.png
diff --git a/docs/source/modules/dwn/basin-river.rst b/docs/source/modules/dwn/basin-river.rst
index 7e30741af3..3b9c57293b 100644
--- a/docs/source/modules/dwn/basin-river.rst
+++ b/docs/source/modules/dwn/basin-river.rst
@@ -1,42 +1,40 @@
Resilient rivers and basins
===========================
-
-Understand forest cover and forest cover change from a watershed perspective
-----------------------------------------------------------------------------
+*Understand forest cover and forest cover change from a watershed perspective*
Overview
________
-The **Resilient rivers and basins** beta app is a tool that describes forest cover and forest cover change from a watershed perspective by calculating statistics across subwatersheds over time; watersheds of interest will be defined by the upstream basin draining to any given point. These processes can be conducted directly in your SEPAL instance without requiring additional resources.
+The **Resilient rivers and basins** beta app is a tool that describes forest cover and forest cover change from a watershed perspective by calculating statistics across subwatersheds over time; watersheds of interest will be defined by the upstream basin draining to any given point. These processes can be conducted directly in your **SEPAL instance** without requiring additional resources.
-To run this module, we recommend initializing a machine with at least 4 GB RAM (a t2 or m2 instance). For more detailed instructions, see `Start instance manually `__.
+To run this module, we recommend initializing a machine with at least 4 GB RAM (a **t2** or **m2** instance). For more detailed instructions, see `Start instance manually `__.
Inputs
______
-Once you have started an instance, `search in the apps tab `_ for the **Resilient rivers and basins** app, which is composed of two main sections:
+Once you have started an instance, `search in the Apps tab `_ for the **Resilient rivers and basins** app, which is composed of two main sections:
-1. **the left pane**, which displays all of the processes (i.e. inputs and results) at the top, and some helpful links (e.g. bug report, wiki, source code) at the bottom; and
+1. **Left pane**, which displays all processes (i.e. inputs and results) at the top, and some helpful links (e.g. bug report, wiki, source code) at the bottom; and
-2. **the right pane**, which displays process and inputs.
+2. **Right pane**, which displays process and inputs.
-By default, the **input drawer** will be active. It is divided into two main panes:
+By default, the **Input drawer** will be active. It is divided into two main panes:
-1. **the left pane**, which displays all of the input parameters to derive statistics.
+1. **Left pane**, which displays all of the input parameters to derive statistics.
-2. **the right pane**, which displays the map view where calculated layers will be loaded.
+2. **Right pane**, which displays the map view where calculated layers will be loaded.
.. thumbnail:: https://raw.githubusercontent.com/sepal-contrib/basin-rivers/master/doc/img/inputs.png
:width: 80%
:title: Inputs view
:group: inputs
-The first input needed is a **coordinate pair**, which will be used to calculate and retrieve all of the upstream sub-basins that drain to that point for any given **hydroBASIN level**.
+The first input needed is a **Coordinate pair**, which will be used to calculate and retrieve all of the upstream sub-basins that drain to that point for any given **HydroBASIN level**.
-To input coordinates, the module has two options: **manual** and **automatic**.
+To input coordinates, the module has two options: **Manual** and **Automatic**.
-To use **manual selection**, enter the latitude and longitude coordinates, then select :code:`Next`. The map will set a blue marker at that point and zoom into the area of interest.
+To use **Manual selection**, enter the latitude and longitude coordinates, then select :code:`Next`. The map will set a blue marker at that point and zoom into the area of interest (AOI).
-To use **automatic selection**, turn off the manual switch and navigate through the map to find your desired area. Select the exact spot (usually a river confluence or a bridge, but terrestrial areas work as well); a blue marker will be displayed.
+To use **Automatic selection**, turn off the **Manual** switch and navigate through the map to find your desired area. Select the exact spot (usually a river confluence or a bridge, but terrestrial areas work as well); a blue marker will be displayed.
.. thumbnail:: https://raw.githubusercontent.com/sepal-contrib/basin-rivers/master/doc/img/coordinates.png
:width: 55%
@@ -45,17 +43,21 @@ To use **automatic selection**, turn off the manual switch and navigate through
.. note::
- When using the **automatic selection** method, the latitude and longitude input text fields will be deactivated. Notice that the coordinates will be automatically synchronized as you move the cursor over the map.
+ When using the **Automatic selection** method, the latitude and longitude input text fields will be deactivated. Notice that the coordinates will be automatically synchronized as you move the cursor over the map.
+
+The next step is to select the **HydroBASIN target level** by using the dropdown list.
+
+**HydroBASINs** are delineated basins in the HydroATLAS database. Their levels range from 5 to 12, where higher numbers indicate smaller basins nested inside larger sub-basins.
-The next step is to select the **HydroBASIN target level** by using the dropdown list. **HydroBASINs** are delineated basins in the HydroATLAS database. Their levels range from 5 to 12, where higher numbers indicate smaller basins nested inside larger sub-basins. For more information about how these data are obtained, see `HydroSHEDS documentation `_.
+For more information about how these data are obtained, see the `HydroSHEDS documentation `_.
-The forest cover change map is based on the `Global Forest Change product `_ (Hansen *et al.*, 2013), retrieved from `Google Earth Engine `_. Created to show forest cover change on a global-scale based on Landsat imagery, the product has forest change data from 2000 to 2001.
+The forest cover change map is based on the `Global Forest Change product `_ (Hansen *et al.*, 2013), retrieved from `Google Earth Engine (GEE) `_. Created to show forest cover change on a global-scale based on Landsat imagery, the product has forest change data from 2000 to 2001.
The **Resilient rivers and basins** app will crop and summarize forest cover statistics for each of the forest cover classes at the selected basin scale (i.e. the HydroBASIN level).
-To begin, use the sliders to select the **start and end year**.
+To begin, use the sliders to select the **Start and end year**.
-Next, select the **forest threshold**, which determines the level of tree cover required for a pixel in the Global Forest Change product to be classified as **forest**. Changing the value will alter the amount of forest cover or forest cover change observed.
+Next, select the **Forest threshold**, which determines the level of tree cover required for a pixel in the Global Forest Change (GFC) product to be classified as **Forest**. Changing the value will alter the amount of forest cover or forest cover change observed.
.. thumbnail:: https://raw.githubusercontent.com/sepal-contrib/basin-rivers/master/doc/img/levels.png
:width: 55%
@@ -65,11 +67,11 @@ Next, select the **forest threshold**, which determines the level of tree cover
Select the :btn:`Get upstream basins` button to run the process. The results will be displayed as a map of forest cover change by sub-basin.
.. note::
- The amount of time required for calculation depends on the selected **HydroBASIN level** and the **location of the downstream point**.
+ The amount of time required for calculation depends on the selected **HydroBASIN level** and the **Location of the downstream point**.
- Also, the number of **sub-basins** displayed will vary depending on the **downstream point** (e.g. a watershed draining to a point at the mouth of a fairly mountainous area will include more upstream sub-basins than a watershed draining to a higher-level point in the same orography).
+ Also, the number of **Sub-basins** displayed will vary depending on the **Downstream point** (e.g. a watershed draining to a point at the mouth of a fairly mountainous area will include more upstream sub-basins than a watershed draining to a higher-level point in the same orography).
-To start a new process, you can use the **trash bin** button in the upper-left of the map to remove the **downstream point** or remove the **sub-basins** selection (for more information, see the next section, which explains how to constrain the analysis to a given set of sub-basins).
+To start a new process, you can use the **Trash bin** button in the upper left of the map to remove the **Downstream point** or remove the **Sub-basins** selection (for more information, see the next subsection, which explains how to constrain the analysis to a given set of sub-basins).
.. thumbnail:: https://raw.githubusercontent.com/sepal-contrib/basin-rivers/master/doc/img/trash_bin.png
:width: 30%
@@ -78,12 +80,12 @@ To start a new process, you can use the **trash bin** button in the upper-left o
To calculate and display statistical results in the **Results** dashboard, use the **Statistics** tile. There are two selection methods:
-1. **no filter** (i.e. use all basins);
-2. **filter**.
+1. **No filter** (i.e. use all basins)
+2. **Filter**
-When using the **Filter** option, a new dropdown menu will appear at the bottom of the tile with all of the sub-basin IDs.
+When using the **Filter** option, a new dropdown menu will appear at the bottom of the tile with all of the sub-basin IDs.
-Manually select or remove **sub-basins** by selecting each row. Notice that the map will automatically sync the selected basins by displaying a black boundary and zooming in.
+Manually select or remove **Sub-basins** by selecting each row. Notice that the map will automatically sync the selected basins by displaying a black boundary and zooming in.
Select the **Calculate statistics** button.
@@ -97,24 +99,24 @@ Once the dashboard is calculated, a red dot will be displayed in the **Results**
Dashboard
_________
-The **Dashboard** panel is divided into three main sections:
+The **Dashboard** pane is divided into three main sections:
-1. the **Settings** tile in the upper-left;
-2. the **Pie chart** in the upper-right; and
-3. **detailed charts** at the bottom.
+1. the **Settings** tile in the upper left;
+2. the **Pie chart** in the upper right; and
+3. **Detailed charts** at the bottom.
.. tip::
- All graphs have an option for independent download directly to your browser. Simply hover the cursor in the upper-right corner and select the :icon:`fa-solid fa-camera` icon.
+ All graphs have an option for independent download directly to your browser. Simply hover the cursor in the upper-right corner and select the :icon:`fa-solid fa-camera` icon.
In the **Settings** tile, you can choose the variable to display:
-- **all**,
-- **gain and loss**,
-- **loss**,
-- **non-forest**,
-- **forest**, and
-- **gain**.
+- **All**
+- **Gain and loss**
+- **Loss**
+- **Non-forest**
+- **Forest**
+- **Gain**
By choosing one of these options, all graphs will display the selected statistics. From this menu, you can also filter the data by one or more sub-basins, allowing the possibility of generating dynamic comparisons between areas.
@@ -130,16 +132,16 @@ The **Overall ratio** is an interactive pie chart that displays the output varia
:title: Overall ratio pie chart
:group: dashboard
-The detailed, interactive charts at the bottom display both the **ratio** and the **total area** of the selected variable.
+The detailed, interactive charts at the bottom display both the **Ratio** and the **Total area** of the selected variable.
-On the left, the **pie chart** shows the proportion of the area for each of the selected sub-basins.
+On the left, the **Pie chart** shows the proportion of the area for each of the selected sub-basins.
-On the right, the **bar chart** displays the absolute values.
+On the right, the **Bar chart** displays the absolute values.
.. note::
- In the Global Forest Change product dataset (Hansen *et al.*, 2013), only forest loss has a temporal dimension. When a new time period is selected, a new graph representing the trend of forest loss will be displayed at the bottom of the screen.
+ In the GFC product dataset (Hansen *et al.*, 2013), only forest loss has a temporal dimension. When a new time period is selected, a new graph representing the trend of forest loss will be displayed at the bottom of the screen.
.. image:: https://raw.githubusercontent.com/sepal-contrib/basin-rivers/master/doc/img/interactive_stats.gif
-.. custom-edit:: https://raw.githubusercontent.com/sepal-contrib/basin-rivers/master/doc/en.rst
+.. custom-edit:: https://raw.githubusercontent.com/sepal-contrib/basin-rivers/release/doc/en.rst
diff --git a/docs/source/modules/dwn/bfast_explorer.rst b/docs/source/modules/dwn/bfast_explorer.rst
index 5b4f0f6e05..be6789259b 100644
--- a/docs/source/modules/dwn/bfast_explorer.rst
+++ b/docs/source/modules/dwn/bfast_explorer.rst
@@ -1,18 +1,19 @@
BFAST Explorer
==============
+*Analyse Landsat SR time-series pixel data*
.. note::
- This tutorial was created using BFAST Explorer v0.0.1. If you are using a newer version, some features might differ.
+ This tutorial was created using **BFAST Explorer v0.0.1**. If you are using a newer version, some features might differ.
Description
-----------
-**BFAST Explorer** is a `Shiny `__ app, developed using R and Python, designed for the analysis of Landsat surface reflectance time-series pixel data.
+**BFAST Explorer** is a `Shiny `__ app, developed using R and Python, designed for the analysis of Landsat surface reflectance time series pixel data (BFAST refers to Breaks for Additive Seasonal and Trend).
-Three change detection algorithms – **bfastmonitor**, **bfast01** and **bfast** – are used in order to investigate temporal changes in trend and seasonal components via breakpoint detection.
+Three change detection algorithms – **bfastmonitor**, **bfast01** and **bfast** – are used to investigate temporal changes in trend and seasonal components via breakpoint detection.
-If you encounter any problems, please send a message to almeida.xan@gmail.com or create an issue on `GitHub `__.
+If you encounter any problems, please send a message to almeida.xan@gmail.com or `create an issue on GitHub `__.
Tutorial
--------
@@ -27,7 +28,9 @@ The **Map** tab is the starting tab that is initially displayed when running the
.. thumbnail:: https://raw.githubusercontent.com/almeidaxan/bfast-explorer/master/md/images/tutorial-01.jpg
:group: bfast-explorer
-Users can also use the **Search** field located at the top of the toolbar to search for a location. The map automatically zooms in on the desired location, similarly to Google Maps. In this example, we searched for :code:`unicamp campinas` (the University of Campinas).
+Users can also use the **Search** field located at the top of the toolbar to search for a location. The map automatically zooms in on the desired location, similar to Google Maps.
+
+In this example, we searched for :code:`unicamp campinas` (the University of Campinas).
.. thumbnail:: https://raw.githubusercontent.com/almeidaxan/bfast-explorer/master/md/images/tutorial-02.jpg
:group: bfast-explorer
@@ -41,7 +44,7 @@ To clear all placed markers, select the red |trash-icon| button on the left side
Next, select one of the markers to download its Landsat pixel data by selecting an already placed marker (it will be highlighted; only one marker may be selected at a time).
-By selecting a marker, you can now choose a combination of which satellites to download from using the dropdown menu, located on the bottom of the toolbar. In the example, we have choosen all of the available satellites products: Landsat 5, 7 and 8 SR.
+By selecting a marker, you can now choose a combination of which satellites to download from using the dropdown menu, located on the bottom of the toolbar. In the example, we have choosen all of the available satellite products: Landsat 5, 7 and 8 SR.
.. thumbnail:: https://raw.githubusercontent.com/almeidaxan/bfast-explorer/master/md/images/tutorial-04.jpg
:group: bfast-explorer
@@ -50,7 +53,7 @@ Then, press the blue |download-icon| :guilabel:`Get data` button, located on the
.. note::
- At the time of writing this documentation page, all surface reflectance data were not available from GEE. Depending on where you place your markers, you may receive the following message: "No data available for the chosen satellite(s) and/or region...Please change your query and try again." Since the SEPAL platform relies on GEE to download data, the SEPAL team can not help with this issue.
+ At the time of writing this article, all surface reflectance data were not available from GEE. Depending on where you place your markers, you may receive the following message: "No data available for the chosen satellite(s) and/or region...Please change your query and try again." Since the SEPAL platform relies on GEE to download data, the SEPAL team can not help with this issue.
.. thumbnail:: https://raw.githubusercontent.com/almeidaxan/bfast-explorer/master/md/images/tutorial-05.jpg
:group: bfast-explorer
@@ -60,47 +63,47 @@ If the download is successful, you'll receive a message directing you to the |ch
Analysis tab
************
-In the **Analysis** tab, we can analyse the downloaded data and save the results locally as files.
+In the **Analysis** tab, we can analyse the downloaded data and save the results locally as files.
.. thumbnail:: https://raw.githubusercontent.com/almeidaxan/bfast-explorer/master/md/images/tutorial-06.jpg
:group: bfast-explorer
-First, choose which satellite time series date to visualize. Even though data was downloaded from Landsat 5, 7 and 8 SR, they can't be analyzed separately. However, let's proceed by choosing all of them.
+First, choose which **Satellite time series date** to visualize. Even though data was downloaded from Landsat 5, 7 and 8 SR, they can't be analysed separately. However, let's proceed by choosing all of them.
The time series of the first spectral band (:code:`b1`) is plotted for all satellites. A legend distinguishes the different sources.
.. note::
- Use caution when comparing **Spectral bands** data from different satellites, as they may not correspond to the same wavelength range (see `here `__).
+ Use caution when comparing **Spectral bands** data from different satellites, as they may not correspond to the same wavelength range (for more information, see `this page `__).
.. thumbnail:: https://raw.githubusercontent.com/almeidaxan/bfast-explorer/master/md/images/tutorial-07.jpg
:group: bfast-explorer
-Apart from the spectral bands, there are also four spectral-bands-derived indexes available: NDVI, NDMI, EVI and EVI2. As an example, let's check the NDVI time series.
+Apart from the spectral bands, there are also four spectral-bands-derived indexes available: **NDVI**, **NDMI**, **EVI** and **EVI2**. As an example, let's check the **NDVI time series**.
.. thumbnail:: https://raw.githubusercontent.com/almeidaxan/bfast-explorer/master/md/images/tutorial-08.jpg
:group: bfast-explorer
-All time-series data can be downloaded as a file by selecting the blue |download-icon| :guilabel:`Data` button. All data will be downloaded as a CSV file, ordered by the acquisiton date. An additional column is included in order to distinguish the satellite sources.
+All time series data can be downloaded as a file by selecting the blue |download-icon| :guilabel:`Data` button. All data will be downloaded as a .csv file, ordered by the acquisiton date. An additional column is included in order to distinguish satellite sources.
.. thumbnail:: https://raw.githubusercontent.com/almeidaxan/bfast-explorer/master/md/images/tutorial-09.jpg
:group: bfast-explorer
-The time series plot can be downloaded as an image by selecting the blue |download-icon| :guilabel:`Plot` button. A window will appear offering some raster (JPEG, PNG) and a vectorial (SVG) image output formats.
+The time series plot can be downloaded as an image by selecting the blue |download-icon| :guilabel:`Plot` button. A window will appear offering some raster (.jpeg, .png) and a vectorial (.svg) image output formats.
.. thumbnail:: https://raw.githubusercontent.com/almeidaxan/bfast-explorer/master/md/images/tutorial-10.jpg
:group: bfast-explorer
-Next, select the **Change detection algorithm**. Three options are available: **bfastmonitor**, **bfast01** and **bfast** (for more information, go to `this page `__).
+Next, select the **Change detection algorithm**. Three options are available: **bfastmonitor**, **bfast01** and **bfast** (for more information, see `this page `__).
.. thumbnail:: https://raw.githubusercontent.com/almeidaxan/bfast-explorer/master/md/images/tutorial-11.jpg
:group: bfast-explorer
-By selecting **bfastmonitor**, you can tweak four parameters in the left sidebar: :code:`formula`, :code:`history period type`, :code:`harmonic order`, and :code:`start of monitoring`. These parameters have different impacts on results, which can be verified on the right side plot. Here, we set the maximum value of the :code:`harmonic order` to 9 to avoid problems.
+By selecting **bfastmonitor**, you can tweak four parameters in the left sidebar: :code:`formula`, :code:`history period type`, :code:`harmonic order`, and :code:`start of monitoring`. These parameters have different impacts on results, which can be verified on the right side plot. Here, we set the maximum value of the :code:`harmonic order` to **9** to avoid problems.
-Similar to the time series, the results of the change detection algorithms as .RDS data files can also be downloaded by selecting the blue |download-icon| :guilabel:`Results` button. To download the plot, select the blue |download-icon| :guilabel:`Plot` button.
+Similar to the time series, the results of the change detection algorithms as .rds data files can also be downloaded by selecting the blue |download-icon| :guilabel:`Results` button. To download the plot, select the blue |download-icon| :guilabel:`Plot` button.
-For more information on how to load RDS files on R, see `this page `__.
+For more information on how to load .rds files on R, see `this page `__.
.. thumbnail:: https://raw.githubusercontent.com/almeidaxan/bfast-explorer/master/md/images/tutorial-12.jpg
:group: bfast-explorer
@@ -114,7 +117,7 @@ Here, the maximum value of the :code:`harmonic order` is set dynamically, depend
Finally, by selecting **bfast**, you can tweak two parameters: :code:`h` (minimal segment size) and :code:`season type`.
-Since **bfast** can detect multiple breakpoints, it may take a couple of seconds to process, in comparison to the previous two algorithms.
+Since **bfast** can detect multiple breakpoints, it may take a couple of seconds to process in comparison to the previous two algorithms.
.. thumbnail:: https://raw.githubusercontent.com/almeidaxan/bfast-explorer/master/md/images/tutorial-14.jpg
:group: bfast-explorer
@@ -131,4 +134,5 @@ Since **bfast** can detect multiple breakpoints, it may take a couple of seconds
-.. custom-edit:: https://raw.githubusercontent.com/12rambau/bfast-explorer/master/md/tutorial.rst
+
+.. custom-edit:: https://raw.githubusercontent.com/sepal-contrib/bfast-explorer/main/md/tutorial.rst
diff --git a/docs/source/modules/dwn/bfast_gpu.rst b/docs/source/modules/dwn/bfast_gpu.rst
index 5ad192f9c3..46b959ca72 100644
--- a/docs/source/modules/dwn/bfast_gpu.rst
+++ b/docs/source/modules/dwn/bfast_gpu.rst
@@ -1,18 +1,23 @@
BFAST GPU
=========
+*GPU implementation of the BFAST algorithm to analyse time series*
-This document provides usage instructions for the GPU implementation of BFAST, implemented here as a Jupyter dashboard on SEPAL.
+This article provides usage instructions for the GPU implementation of Breaks for Additive Seasonal and Trend (BFAST), implemented here as a Jupyter dashboard on SEPAL.
Introduction
------------
-Large amounts of satellite data are now becoming available, which, in combination with appropriate change detection methods, offer the opportunity to derive accurate information on timing and location of disturbances (e.g. deforestation events across the Earth's surface). Typical scenarios require the analysis of billions of image patches/pixels, which can become very expensive from a computational point of view. The `BFAST package `_ provides efficient, massively parallel implementation for one of the state-of-the-art change detection methods called `Breaks For Additive Season and Trend (BFASTmonitor) `, as proposed by Verbesselt *et al.*
+Large amounts of satellite data are now becoming available, which, in combination with appropriate change detection methods, offer the opportunity to derive accurate information on timing and location of disturbances (e.g. deforestation events across the Earth's surface).
+
+Typical scenarios require the analysis of billions of image patches/pixels, which can become very expensive from a computational point of view.
+
+The `BFAST package `_ provides efficient, massively parallel implementation for one of the state-of-the-art change detection methods called `BFASTmonitor `, as proposed by Verbesselt *et al.*
.. figure:: https://raw.githubusercontent.com/12rambau/bfast_gpu/master/doc/img/bfastmonitor_1.png
The analysis perfomed pixel-wise
-The implementation is based on `OpenCL `_. It allows the processing of large-scale change detection scenarios given satellite time-series data. The optimizations made are tailored to the specific requirements of modern, massively parallel devices, such as graphics processing units (GPUs).
+The implementation is based on `OpenCL `_. It allows the processing of large-scale change detection scenarios given satellite time series data. The optimizations made are tailored to the specific requirements of modern, massively parallel devices, such as graphics processing units (GPUs).
The full documentation of the :code:`bfast` package can be found `on this page `_.
@@ -21,7 +26,7 @@ Usage
.. important::
- Before launching the BFAST module, you need to have at least one time series in your SEPAL folders.
+ Before launching the **BFAST** module, you need to have at least one time series in your **SEPAL folders**.
.. attention::
@@ -32,9 +37,13 @@ Set up
To launch the app, follow the `steps to register for SEPAL `_.
-Open a GPU instance in your terminal (:code:`g4` or :code:`g8`). Then move to the SEPAL **Apps** dashboard (purple wrench icon on the left side panel). Finally, search for and select **bfast GPU**.
+Open a **GPU instance** in your terminal (:code:`g4` or :code:`g8`).
+
+Then move to the SEPAL **Apps** dashboard (purple wrench icon on the left side pane).
-The application should launch itself in the **BFAST process** section. On the left side, the **navdrawer** will help you navigate between the different pages of the app. If you click on :code:`wiki`, :code:`bug report` or :code:`code source`, you will be redirected to the corresponding webpage.
+Finally, search for and select **bfast GPU**.
+
+The application should launch itself in the **BFAST process** section. On the left side, the **navdrawer** will help you navigate between the different pages of the app. If you select :code:`wiki`, :code:`bug report` or :code:`code source`, you will be redirected to the corresponding webpage.
.. note::
@@ -47,18 +56,20 @@ The application should launch itself in the **BFAST process** section. On the le
Select folder
^^^^^^^^^^^^^
-Select a folder in the first widget. Navigate through your folders to find the time series folder you want to analyse. Click outside the pop-up window to exit the selection. Your folder should only contain folders with numbered names (corresponding to each tile of the TS).
+Select a folder in the first widget.
+
+Navigate through your folders to find the time series folder you want to analyse. Click outside the pop-up window to exit the selection. Your folder should only contain folders with numbered names (corresponding to each tile of the TS).
-By selecting an appropriate folder, a widget will be automatically filled out and enabled, as described below:
+By selecting an appropriate folder, a widget will be automatically completed and enabled, as described below:
-- :code:`output directory name`: Filled out with the basename of your TS folder.
-- :code:`select tiles to use`: Preselect all of the available tiles.
-- :code:`monitoring dates`: The slider is enabled and filled with the date list included in the TS folder. The values default to the full range of dates.
-- :code:`historical period`: The slider is enabled and filled with the date list included in the TS folder. The value defaults to the first date of the TS.
+- :code:`output directory name`: Completed with the basename of your TS folder.
+- :code:`select tiles to use`: Preselect all available tiles.
+- :code:`monitoring dates`: The slider is enabled and completed with the date list included in the TS folder (the values default to the full range of dates).
+- :code:`historical period`: The slider is enabled and completed with the date list included in the TS folder (the value defaults to the first date of the TS).
.. figure:: https://raw.githubusercontent.com/12rambau/bfast_gpu/master/doc/img/correct_folder.png
- Select a folder and preload all of the information.
+ Select a folder and preload all information.
.. attention::
@@ -73,7 +84,7 @@ Parameters
Now you can change the parameters to fit the requirements of your analysis:
-- :code:`output directory name`: This name will be used to store all of your analysis. While it is completed automatically with the base name of your TS folder, you can still change it.
+- :code:`output directory name`: This name will be used to store all of your analysis. While it is completed automatically with the base name of your TS folder, you can change it.
.. note::
@@ -100,10 +111,10 @@ Advanced parameters
These parameters are for advanced users only. The default value provides accurate results in many situations.
-Select :code:`Advanced parameters` and a new panel of options will be available:
+Select :code:`Advanced parameters` and a new pane of options will be available:
- :code:`bandwith relative to sample size`: Float in the interval (0,1), specifying the bandwidth relative to the sample size in the MOSUM/ME monitoring processes.
-- :code:`Significance level of the monitoring`: Significance level of the monitoring procedure (and ROC, if selected), i.e. probability of Type I error.
+- :code:`Significance level of the monitoring`: Significance level of the monitoring procedure and ROC, if selected (i.e. probability of Type I error).
- :code:`backend`: Specifies the implementation that shall be used: **Python** resorts to the non-optimized Python version; **OpenCL** resorts to the optimized, massively parallel OpenCL implementation.
.. note::
@@ -119,7 +130,7 @@ Run process
You can now select :code:`LAUNCH BFAST ANALYSIS` to start the process.
-The process will start shortly, notifying you of it's advancement tile by tile in the info banner, as shown in the following figure.
+The process will start shortly, notifying you of it's advancement tile by tile in the **Info banner**, as shown in the following figure.
.. figure:: https://raw.githubusercontent.com/12rambau/bfast_gpu/master/doc/img/advancement.png
@@ -133,20 +144,18 @@ The process will start shortly, notifying you of it's advancement tile by tile i
If your connection to SEPAL is lost and the application stops, use the exact same parameters as your previous analysis. The application will find the already computed tiles and images, and start from where it stopped instead of restarting from scratch.
-
.. figure:: https://raw.githubusercontent.com/12rambau/bfast_gpu/master/doc/img/computation_end.png
End of computation screen
-
The module provided the following :code:`.vrt` outputs:
- :code:`~/module_results/bfast/[name_of_input]/[bfast_params]/bfast_outputs.vrt`
It is a three-band raster:
-- band 1, the breakpoints in decimal year format
-- band 2, the magnitude of change
-- band 3, the validity of the pixel model
+- **Band 1**: the breakpoints in decimal year format
+- **Band 2**: the magnitude of change
+- **Band 3**: the validity of the pixel model
This raster has the exact same dimensions as the input raster.
@@ -161,6 +170,6 @@ Here you'll find an example of two bands over the Juaboso Region in Ghana with a
.. figure:: https://raw.githubusercontent.com/12rambau/bfast_gpu/master/utils/breaks.png
- Breaks masked in the center of the region, displayed with a viridis colormap, values in [2017.26, 2019.98]
-
+ Breaks masked in the centre of the region, displayed with a viridis colormap, values in [2017.26, 2019.98]
+
.. custom-edit:: https://raw.githubusercontent.com/sepal-contrib/bfast_gpu/release/doc/en.rst
diff --git a/docs/source/modules/dwn/bfast_r.rst b/docs/source/modules/dwn/bfast_r.rst
index f51502b482..01226f482d 100644
--- a/docs/source/modules/dwn/bfast_r.rst
+++ b/docs/source/modules/dwn/bfast_r.rst
@@ -1,20 +1,21 @@
Time series analysis
====================
+*Analyse the dynamics of satellite dense time series and overcome the major challenge of distinguishing land-cover change from seasonal phenological variations*
Overview
--------
-The Breaks for Additive Seasonal and Trend (BFAST) method enables users to analyse the dynamics of satellite dense time series and overcome the major challenge of distinguishing land-cover change from seasonal phenological variations.
+The Breaks for Additive Seasonal and Trend (BFAST) method enables users to analyse the dynamics of satellite dense time series and overcome the major challenge of distinguishing land cover change from seasonal phenological variations.
-`Verbesselt et al. (2010) `__, `Dutrieux et al. (2015) `__ and `DeVries et al. (2015) `__ used this approach to demonstrate that time series can be decomposed into trend, seasonal, and remainder components, and that the time and number of changes can be detected at high temporal resolution (i.e. 16 days), enabling detection of tree cover change and separation from the phenology signal.
+`Verbesselt et al. (2010) `__, `Dutrieux et al. (2015) `__ and `DeVries et al. (2015) `__ used this approach to demonstrate that time series can be decomposed into trend, seasonal and remainder components, and that the time and number of changes can be detected at high temporal resolution (i.e. 16 days), enabling detection of tree cover change and separation from the phenology signal.
-The same authors developed the `bfastSpatial package `__, which provides utilities to perform change detection analysis on time series of spatial gridded data, such as the Landsat satellite imagery that cover our period of interest.
+The same authors developed the `bfastSpatial package `__, which provides utilities to perform change detection analysis on time series of spatial gridded data, such as the Landsat satellite imagery that covers our period of interest.
-The package has been tested in the early versions of the cloud-based platform developed by the Food and Agriculture Organization of the United Nations (FAO) for parallel processing of remote sensing data (https://sepal.io). It has been recently adapted into a functional processing chain (https://github.com/yfinegold/runBFAST) which is wrapped in the current Shiny application.
+The package has been tested in the early versions of the cloud-based platform developed by the Food and Agriculture Organization of the United Nations (FAO) for parallel processing of remote sensing data (https://sepal.io). It has been recently adapted into a functional processing chain (https://github.com/yfinegold/runBFAST), which is wrapped in the current Shiny application.
.. attention::
- Since the processing of time series using BFAST is computed in SEPAL, selecting a powerful instance is recommended. The computation is parallelized on several central processing unit (CPUs), so an instance with a large number of CPUs is recommended as well. Select the terminal button and choose a new instance. Instance ”m16” with 16 CPUs, might be fast enough to do the processing.
+ Since the processing of time series using BFAST is computed in SEPAL, selecting a powerful instance is recommended. The computation is parallelized on several central processing units (CPUs), so an instance with a large number of CPUs is recommended as well. Select the terminal button and choose a new instance. Instance ”m16” with 16 CPUs might be fast enough to do the processing.
Introduction
------------
@@ -38,7 +39,7 @@ If this is your first time using the time series analysis tool, the SEPAL team h
Select :guilabel:`Download test dataset`. The data is downloaded into the :code:`bfast_data_test` folder in your root directory. The file location and information about the download will appear in the lower-right corner.
-Run time-series analysis
+Run time series analysis
------------------------
Select the **Process** tab in the left column.
@@ -46,7 +47,7 @@ Select the **Process** tab in the left column.
.. thumbnail:: https://raw.githubusercontent.com/12rambau/bfastspatial/master/www/tutorial/img/process_tab.png
:group: bfastspatial
-First, select the **Time series** folder. Click on the **Time series** folder button to navigate to the folder with your downloaded data (either downloaded from the SEPAL search option or the test dataset).
+First, select the **Time series** folder. Select the **Time series** folder button to navigate to the folder with your downloaded data (either downloaded from the **SEPAL search option** or the **Test dataset**).
Select the whole **Time series** folder in your download folder. If your area of interest (AOI) had different features, the application will ask you to select which one you want to process (you can select one, some of them, or all of them) (see the example below with 140 distinct features).
@@ -56,12 +57,12 @@ Select the whole **Time series** folder in your download folder. If your area of
.. thumbnail:: https://raw.githubusercontent.com/12rambau/bfastspatial/master/www/tutorial/img/select_ts_tile.png
:group: bfastspatial
-There is an option to apply a mask and run BFAST only on areas inside the mask. You can select a file with 0 and 1 values (0 values will be excluded and 1 included in the computation).
+There is an option to apply a mask and run **BFAST** only on areas inside the mask. You can select a file with 0 and 1 values (0 values will be excluded and 1 included in the computation).
.. thumbnail:: https://raw.githubusercontent.com/12rambau/bfastspatial/master/www/tutorial/img/select_mask.png
:group: bfastspatial
-If you would like to use a mask, select the **FNF mask**. Then, select the raster file by selecting the **forest/non-forest** mask button and navigating to and selecting the mask file.
+If you would like to use a mask, select the **FNF mask**. Then, select the raster file by selecting the **forest/non-forest mask** button and navigating to and selecting the mask file.
.. thumbnail:: https://raw.githubusercontent.com/12rambau/bfastspatial/master/www/tutorial/img/fnf_mask.png
:group: bfastspatial
@@ -69,11 +70,11 @@ If you would like to use a mask, select the **FNF mask**. Then, select the raste
.. thumbnail:: https://raw.githubusercontent.com/12rambau/bfastspatial/master/www/tutorial/img/browse_mask.png
:group: bfastspatial
-Next, change the parameters for your study area. At this stage, the BFAST explorer described in Section 2 can be very useful. You can use it to understand seasonal and interannual patterns of the land cover that you are analysing over your study area. You can do this over several pixels to have a better idea.
+Next, change the **Parameters** for your study area. At this stage, the BFAST Explorer described in **Section 2** can be very useful. You can use it to understand seasonal and interannual patterns of the land cover that you are analysing over your study area. You can do this over several pixels to have a better idea.
.. note::
- Remember that this module will define a historical period and a monitoring period, so it corresponds to the option “bfastmonitor” in the BFAST explorer module.
+ Remember that this module will define a historical period and a monitoring period, so it corresponds to the option “bfastmonitor” in the **BFAST Explorer** module.
.. thumbnail:: https://raw.githubusercontent.com/12rambau/bfastspatial/master/www/tutorial/img/parameters.png
:group: bfastspatial
@@ -82,34 +83,34 @@ The parameters include:
- **History beginning year** – The year that marks the start of the historical period. The actual start date will depend on the history parameter chosen.
- **Monitoring start and end years** – The monitoring start year is the year that marks the end of the historical period and the start of the monitoring period. The monitoring end year marks the end of the monitoring period.
-- **History parameter** – Specifies the start of a stable history period. The options are:
+- **History parameter** – Specifies the start of a stable historical period. The options are:
- - reverse ordered CUSUM (ROC) – looks backwards in time, using a stepwise approach, to identify a stable history period.
- - Bai and Perron breakpoint estimation (BP) – identifies a stable history period and can be used to identify disturbances in the historical period.
- - all – uses all available observations.
- - numeric – the start date can be specified using the year (e.g. 2011).
+ - **reverse ordered CUSUM (ROC)** – looks backwards in time, using a stepwise approach, to identify a stable historical period.
+ - **Bai and Perron breakpoint estimation (BP)** – identifies a stable historical period and can be used to identify disturbances in the historical period.
+ - **all** – uses all available observations.
+ - **numeric** – the start date can be specified using the year (e.g. 2011).
- **Elements of the formula** – the formula describes the type of regression model applied. The options are:
- - trend + harmon – a linear trend and a harmonic season component
- - harmon – a harmonic season component
- - trend – a linear trend
+ - **trend + harmon** – a linear trend and a harmonic season component.
+ - **harmon** – a harmonic season component.
+ - **trend** – a linear trend.
- **Order parameter** – Specifies the order of the harmonic term, defaulting to 3.
-- **Type parameter** – Specifies the type of monitoring process (for additional documentation on the type parameter see the `strucchange package documentation `__). The options are:
+- **Type parameter** – Specifies the type of monitoring process (for additional documentation on the type parameter, see the `strucchange package documentation `__). The options are:
- - Moving sums of residuals (MOSUM) – residuals are calculated as the difference between expected values and actual observations in a monitoring period based on OLS residuals.
- - Cumulative sum (CUSUM) – cumulative sums of standardized residuals (MOSUM uses a moving sum, while CUSUM uses a cumulative of the same residuals).
- - Moving estimates (ME) – the moving estimates process is returned.
- - Fluctuation – returns the recursive estimates process.
+ - **Moving sums of residuals (MOSUM)** – residuals are calculated as the difference between expected values and actual observations in a monitoring period based on OLS residuals.
+ - **Cumulative sum (CUSUM)** – cumulative sums of standardized residuals (MOSUM uses a moving sum, while CUSUM uses a cumulative of the same residuals).
+ - **Moving estimates (ME)** – the moving estimates process is returned.
+ - **Fluctuation** – returns the recursive estimates process.
-- **Raster band outputs** – Result layers to be returned. Can be any combination of :code:`breakpoint`, :code:`magnitude`, :code:`error`, :code:`history`, :code:`r.squared`, :code:`adj.r.squared`, :code:`coefficients`. By default: :code:`breakpoint`, :code:`magnitude` and :code:`error` are returned by the function. It is important to know which layers have been requested and in which order they will be exported because the layer names are not specified. Note that if :code:`coefficients` is included, the output will include the following: "(Intercept)" and any trend and/or harmonic coefficients, depending on the values of formula and order.
-- **Computation mode** – chose between running the calculation for the entire monitoring period (overall) or each year of the monitoring period (sequential):
+- **Raster band outputs** – Result layers to be returned. Can be any combination of :code:`breakpoint`, :code:`magnitude`, :code:`error`, :code:`history`, :code:`r.squared`, :code:`adj.r.squared`, :code:`coefficients`. By default: :code:`breakpoint`, :code:`magnitude` and :code:`error` are returned by the function. It is important to know which layers have been requested and in which order they will be exported because the layer names are not specified. Note that if :code:`coefficients` is included, the output will include the following: **(Intercept)** and any trend and/or harmonic coefficients, depending on the values of formula and order.
+- **Computation mode** – choose between running the calculation for the entire monitoring period (overall) or each year of the monitoring period (sequential):
- - Overall – runs BFAST one time for the monitoring period and provides a maximum of one breakpoint for the entire monitoring period.
- - Sequential – runs BFAST for each year of the monitoring period. The output will be per year of the monitoring period and will provide a maximum of one breakpoint per year in the monitoring period. This option does not create the thresholded output and will not display the output within the application. To view the results, use the visualizer in SEPAL or download the results to your local computer.
+ - **Overall** – runs BFAST one time for the monitoring period and provides a maximum of one breakpoint for the entire monitoring period.
+ - **Sequential** – runs BFAST for each year of the monitoring period. The output will be per year of the monitoring period and will provide a maximum of one breakpoint per year in the monitoring period. This option does not create the thresholded output and will not display the output within the application. To view the results, use the visualizer in SEPAL or download the results to your local computer.
-Once you have decided on your parameters, run BFAST by selecting the Launch BFAST calculation button in the results box.
+Once you have decided on your parameters, run BFAST by selecting the **Launch BFAST calculation** button in the **Results** box.
.. thumbnail:: https://raw.githubusercontent.com/12rambau/bfastspatial/master/www/tutorial/img/launch.png
:group: bfastspatial
@@ -119,7 +120,7 @@ Depending on the size of your area and the size of your instance, BFAST can take
.. thumbnail:: https://raw.githubusercontent.com/12rambau/bfastspatial/master/www/tutorial/img/log.png
:group: bfastspatial
-If your AOI has multiple polygons and contains many numeric folders (i.e. 1, 2, 3, etc.), it will run the BFAST calculation for each of the folders recursively.
+If your AOI has multiple polygons and contains many numeric folders, it will run the BFAST calculation for each of the folders recursively.
If you are running a large area or have a weak internet connection, which might cause the application to disconnect, you can go to your **User resources** in SEPAL and set the amount of time your session should stay open (see following image), which allows you to shut down SEPAL without stopping the calculation.
@@ -137,20 +138,22 @@ If you have a small study area or have the time, you can wait for the algorithm
When the calculation is complete, you will see the text: :code:`Done processing!!! Click on DISPLAY THE RESULTS`. Select the :guilabel:`Display BFAST results from this session` button to display the thresholded magnitude.
-By default, the output from BFAST includes 3 bands: the breakpoint, magnitude and error. An additional output is calculated in this application, which is the thresholded magnitude. The thresholded magnitude is calculated using the magnitude output, calculating the mean magnitude value over the AOI and applying thresholds of up to +/- 4 standard deviations from the mean. This layer indicates the positive or negative intensity of change of each pixel. Above 2 standard deviations, you can interpret that a change has certainly occurred compared to the historical period modelled.
+By default, the output from BFAST includes 3 bands: the **breakpoint**, **magnitude** and **error**.
+
+An additional output is calculated in this application, which is the **thresholded magnitude**. The thresholded magnitude is calculated using the magnitude output, calculating the mean magnitude value over the AOI and applying thresholds of up to +/- 4 standard deviations from the mean. This layer indicates the positive or negative intensity of change of each pixel. Above 2 standard deviations, you can interpret that a change has certainly occurred compared to the historical period modelled.
.. thumbnail:: https://raw.githubusercontent.com/12rambau/bfastspatial/master/www/tutorial/img/preview.png
:group: bfastspatial
.. note::
- If you are not using the instance anymore to process additional time series, please close the instance by selecting the trashbin button.
+ If you are not using the instance anymore to process additional time series, please close the instance by selecting the **Trashbin** button.
You can also download your results to your hard drive using FileZilla (e.g. ArcGIS).
Here are some examples of how layers can be displayed:
-BFAST was computed over the following area in Indonesia over the years 2013–2019. The years 2013–2016 were used as the historical period and 2016–2019 as the monitoring period.
+BFAST was computed over the following area in Indonesia over the years 2013–2019 (the years 2013–2016 were used as the historical period and 2016–2019 as the monitoring period).
.. thumbnail:: https://raw.githubusercontent.com/12rambau/bfastspatial/master/www/tutorial/img/result_rgb.png
:group: bfastspatial
@@ -170,9 +173,10 @@ BFAST was computed over the following area in Indonesia over the years 2013–20
.. thumbnail:: https://raw.githubusercontent.com/12rambau/bfastspatial/master/www/tutorial/img/result_band_3.png
:group: bfastspatial
-Finally, you will find an additional layer called “threshold”. The thresholded magnitude is calculated using the magnitude output, calculating the mean magnitude value over the AOI and applying thresholds of up to +/- 4 standard deviations from the mean. The layer is a thematic, classification map which has values ranging from 0–10, corresponding to the legend below (you can see how to name them in the following figure).
+Finally, you will find an additional layer called **Threshold**. The thresholded magnitude is calculated using the magnitude output, calculating the mean magnitude value over the AOI and applying thresholds of up to +/- 4 standard deviations from the mean. The layer is a thematic, classification map which has values ranging from 0–10, corresponding to the legend below (you can see how to name them in the following figure).
.. thumbnail:: https://raw.githubusercontent.com/12rambau/bfastspatial/master/www/tutorial/img/result_sigma.png
:group: bfastspatial
-.. custom-edit:: https://raw.githubusercontent.com/12rambau/bfastspatial/master/www/tutorial/tutorial.rst
+
+.. custom-edit:: https://raw.githubusercontent.com/sepal-contrib/bfastspatial/main/www/tutorial/tutorial.rst
diff --git a/docs/source/modules/dwn/bootstrap_sampling_simulator.rst b/docs/source/modules/dwn/bootstrap_sampling_simulator.rst
deleted file mode 100644
index 64d03d08bf..0000000000
--- a/docs/source/modules/dwn/bootstrap_sampling_simulator.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-bootstrap_sampling_simulator
-============================
-
-.. warning::
-
- The english documentation of the module have is under construction.
-
-.. tip::
-
- For specific help, please open an issue on our repository by clicking on this `link `__.
-
-.. custom-edit:: https://github.com/openforis/sepal-doc/blob/master/docs/source/_templates/no_module.rst
diff --git a/docs/source/modules/dwn/clip-time-series.rst b/docs/source/modules/dwn/clip-time-series.rst
index 093d208d8a..650fa553ae 100644
--- a/docs/source/modules/dwn/clip-time-series.rst
+++ b/docs/source/modules/dwn/clip-time-series.rst
@@ -1,36 +1,35 @@
Clip time series
================
+*Download an automatically generated time series from customizable dates as a .pdf file*
-.. tip::
-
- This article should explain every step to execute the module. However, if you encounter a problem, please `report it `_.
+This module allows users to download an automatically generated time series from customizable dates as a :code:`.pdf`. Each mosaic will be represented as a custom square around the point of interest using the band combination selected by the user (ranging in size from 500 m x 500 m to 1000 km x 10000 km).
-This module allows users to download an automatically generated time series from customizable dates as a :code:`.pdf`.
+.. note::
-Each mosaic will be represented as a custom square around the point of interest using the band combination selected by the user (ranging in size from 500 m x 500 m to 1000 km x 10000 km).
+ This article should explain every step to execute the module. However, if you encounter a problem, please `report it `_.
Select file
-----------
-First the user needs to select a file, which will be the main input of the module; each page of the final :code:`.pdf` will match a geometric shape of the input. The user can use two types of inputs:
+First, select a file to be the main input of the module. Each page of the final :code:`.pdf` will match a geometric shape of the input. The user can use two types of inputs:
-- Table file (:code:`.csv`, :code:`.txt`), containing at least coordinates and ID columns
-- Shapes (:code:`.geojson`, :code:`.shp`, :code:`.geopackage`), with at least geometry and ID columns
+- **Table file** (:code:`.csv`, :code:`.txt`) containing at least coordinates and ID columns
+- **Shapes** (:code:`.geojson`, :code:`.shp`, :code:`.geopackage`) with at least geometry and ID columns
Table
*****
Select the :guilabel:`point` radio button.
-The table file can be a :code:`.csv` or :code:`.txt` file. It needs to have at least three columns, including the latitude coordinates, the longitude coordinates, and an ID. There are no restrictions for column names.
+The table file can be a :code:`.csv` or :code:`.txt` file. It needs to have at least three columns, including the latitude coordinates, the longitude coordinates and an ID. There are no restrictions for column names.
.. attention::
- The table coordinates need to remain unprojected (i.e. in EPSG:4326)
+ The table coordinates need to remain unprojected (i.e. in EPSG:4326).
Select :guilabel:`Table file`. Only the matching file type will be displayed. Navigate through your **SEPAL folders** to find the appropriate table.
-Once a file is selected, the widget will try to autopopulate the ID, latitude, and longitude columns. If columns are incorrectly set or if data are missing, select one of the file columns to completely describe the points (x, y, ID).
+Once a file is selected, the widget will try to autopopulate the ID, latitude and longitude columns. If columns are incorrectly set or if data are missing, select one of the **File columns** to completely describe the points (x, y, ID).
.. thumbnail:: https://raw.githubusercontent.com/openforis/clip-time-series/master/doc/img/input_table.png
:alt: input table
@@ -65,7 +64,7 @@ The ID column will be used to name the points in the final .pdf. Select it in th
.. thumbnail:: https://raw.githubusercontent.com/openforis/clip-time-series/master/doc/img/input_shape.png
:alt: input_shape
- :title: Input selector using a shapefile dataset. "Name" has been selected as ID column.
+ :title: Input selector using a shapefile dataset; **Name** has been selected as ID column.
:group: clip-time-serie
Select :guilabel:`load your pts file` to load the shapes as a geodataframe in the app model and display them on a map. The map will be updated with the selected shapes and zoom in on the AOI.
@@ -85,8 +84,8 @@ Drivers
Two drivers are available in this module. You can select either:
-- a GEE-based computation (images will be retreived from GEE), or
-- Planet (images will be retrieved from Planet servers using the user API key).
+- **GEE-based computation** (images will be retreived from GEE), or
+- **Planet** (images will be retrieved from Planet servers using the user API key).
If the user selects :guilabel:`gee`, the panel will ask you to select the satellites to use for thumbnails. Select any satellite imagery from the Landsat family and Sentinel programme.
@@ -97,12 +96,12 @@ The best available image is then selected using the following hierarchical order
- Landsat 5
- Landsat 7
-If the user selects :guilabel:`planet`, the panel will ask for the Planet API key.
+If the user selects :guilabel:`planet`, the pane will ask for the **Planet API key**.
Points
******
-The number of points a user wants to display can vary. If the user selects all, then all available points in the provided file will be used. It's also possible to select a subset of them using their ID names.
+The number of points a user wants to display can vary. If the user selects all, all available points in the provided file will be used. It's also possible to select a subset of them using their ID names.
Bands
*****
@@ -111,17 +110,17 @@ Multiple band combinations can be selected:
- Using the :code:`gee` driver:
- - Red, Green, Blue
- - Nir, Red, Green
- - Nir, Swir1, Red
- - Swir2, Nir, Red
- - Swir2, Swir1, Red
- - Swir2, Nir, Green
+ - **Red, Green, Blue**
+ - **Nir, Red, Green**
+ - **Nir, Swir1, Red**
+ - **Swir2, Nir, Red**
+ - **Swir2, Swir1, Red**
+ - **Swir2, Nir, Green**
- Using the :code:`planet` driver:
- - rgb
- - cir
+ - **rgb**
+ - **cir**
Mosaics
*******
@@ -139,14 +138,14 @@ Using the :code:`gee` driver, mosaics are yearly cloudless mosaics built on the
Using the :code:`planet` driver, three types of mosaics can be selected (and mixed together):
-- NICFI bianual mosaics
-- NICFI monthly mosaics
-- Other (any other mosaics associated with the user API key)
+- **NICFI biannual mosaics**
+- **NICFI monthly mosaics**
+- **Other** (any other mosaics associated with the user API key)
Thumbnails
**********
-Select a thumbnail size, which will be the minimal size of the thumbnail used. If the shape defined in the first panel is bigger, the software will try to find the smallest square around the shape, centred on its centroid.
+Select a thumbnail size, which will be the minimum size of the thumbnail used. If the shape defined in the first pane is bigger, the software will try to find the smallest square around the shape, centred on its centroid.
.. attention::
@@ -159,12 +158,12 @@ In the middle of the final image, the software will display a small square to vi
If the used dataset is a shapefile, the square will be replaced by shape geometry.
-When selecting the validation button, the module provides a summary of the download, which is a warning step to avoid downloading massive numbers of points on incorrectly defined parameters.
+When selecting the **Validation** button, the module provides a summary of the download (a warning step to avoid downloading massive numbers of points on incorrectly defined parameters).
.. thumbnail:: https://raw.githubusercontent.com/openforis/clip-time-series/master/doc/img/viz_gee.png
:alt: viz
:group: clip-time-series
- :title: An example set of parameters to create a .pdf file; data summary can be found in the orange rectangle
+ :title: An example set of parameters to create a .pdf file; data summary can be found in the orange rectangle.
Export data
-----------
@@ -178,7 +177,7 @@ Select the only available button to send your images to GEE or Planet.
.. thumbnail:: https://raw.githubusercontent.com/openforis/clip-time-series/master/doc/img/process_loading.png
:alt: process_loading
:group: clip-time-series
- :title: The progress bar of a downloading process
+ :title: The **Progress** bar of a downloading process
.. note::
diff --git a/docs/source/modules/dwn/coverage_analysis.rst b/docs/source/modules/dwn/coverage_analysis.rst
index cf4c4d252c..829d6c4dff 100644
--- a/docs/source/modules/dwn/coverage_analysis.rst
+++ b/docs/source/modules/dwn/coverage_analysis.rst
@@ -1,9 +1,12 @@
Coverage analysis tool for optical data
=======================================
+*Check per-pixel data availability*
This module uses the `sepal_ui `_ framework and interactive **Voila** dashboard to create maps of cloud-free observations for major optical satellites available on the Google Earth Engine (GEE) platform.
-The framework follows the logic of BFAST's countObs and summaryBrick functions as described `here `_. For more information about BFAST, see `Schultz et al. (2013) `_.
+The framework follows the logic of BFAST's countObs and summaryBrick functions, as described `here `_ (BFAST refers to Breaks For Additive Season and Trend).
+
+For more information about BFAST, see `Schultz et al. (2013) `_.
The three-step process is described in the following sections.
@@ -18,17 +21,17 @@ Using the provided **AOI** selector, select an AOI from the available methods. W
.. note::
- If a custom AOI from a shape or drawing is selected, you will be able to use it directly. The upload to GEE will be launched in the background.
+ If a custom AOI from a shape or drawing is selected, you will be able to use it directly (the upload to GEE will be launched in the background).
Select dataset parameters
-------------------------
To perform BFAST pre-analysis, provide the tool with key parameters:
-- **Date range**: the start and end date of your analysis
+- **Date range**: the start and end dates of your analysis
- **Sensors**: the list of sensors you want to use (Landsat missions and Sentinel-2)
- **Tier 2**: Tier 2 images of the Landsat missions (note: this might lead to incorrect results)
-- **SR**: whether to use surface reflectance (SR) images (by default, top of atmosphere [TOA])
+- **SR**: whether to use surface reflectance (SR) images (by default, TOA, referring to top of atmosphere)
Once all parameters have been chosen, select the button.
@@ -43,8 +46,8 @@ Select one of the statistical measures to display in the following list:
- cloud-free pixel count
- total pixel count (i.e. scene coverage)
-- NDVI Median
-- NDVI Std. Dev.
+- NDVI Median (normalized difference vegetation index median)
+- NDVI Std. Dev. (normalized difference vegetation index standard deviation)
You can also produce stats on a yearly basis using the provided switch.
@@ -57,23 +60,26 @@ You can also produce stats on a yearly basis using the provided switch.
Export dataset
--------------
-When you're satisifed with the displayed information, it can be exported for further use in GIS software or a GEE process. The tool provides two main exportation options: as an asset (in GEE) or a .tif file (in SEPAL).
+When you're satisifed with the displayed information, it can be exported for further use in GIS software or a GEE process. The tool provides two main exportation options:
+
+- as an asset (in GEE), or
+- a .tif file (in SEPAL).
Both use the GEE export system and share the same set of parameters:
-- statistical measures to export
+- **Statistical measures to export**
- count of cloud-free observation per pixel
- NDVI's median of cloud-free observations
- NDVI's std. dev. of cloud-free observations
- count for all observations per pixel
-- time-period
+- **Time period**
- full timespan calculation(s)
- annual calculation(s)
-- scale: the resolution (in metres) to use in the exported GEE file
+- **Scale**: the resolution (in metres) to use in the exported GEE file
.. figure:: https://raw.githubusercontent.com/BuddyVolly/coverage_analysis/main/doc/img/export.png
diff --git a/docs/source/modules/dwn/cusum.rst b/docs/source/modules/dwn/cusum.rst
index 9613cd9821..b345c69e43 100644
--- a/docs/source/modules/dwn/cusum.rst
+++ b/docs/source/modules/dwn/cusum.rst
@@ -1,12 +1,6 @@
cusum
=====
-.. warning::
+.. note::
- The english documentation of the module have is under construction.
-
-.. tip::
-
- For specific help, please open an issue on our repository by clicking on this `link `__.
-
-.. custom-edit:: https://github.com/openforis/sepal-doc/blob/master/docs/source/_templates/no_module.rst
+ This article of SEPAL documentation is not yet available. To request that it be made available, use the `GitHub Issue Tracker `__.
diff --git a/docs/source/modules/dwn/damage_proxy_map.rst b/docs/source/modules/dwn/damage_proxy_map.rst
index 3f7363ca47..4e3d491d00 100644
--- a/docs/source/modules/dwn/damage_proxy_map.rst
+++ b/docs/source/modules/dwn/damage_proxy_map.rst
@@ -1,13 +1,10 @@
Damage proxy maps
=================
+*Create damage proxy maps based on the CCD method with Sentinel-1 SLC data*
-**Damage proxy maps based on coherence change detection**
+This module provides a fully automated workflow for the creation of damage proxy maps based on the method of coherent change detection (CCD) with Sentinel-1 SLC data, as described by `Tay et al. (2020) `_ (SLC refers to Single Look Complex).
-This module provides a fully-automated workflow for the creation of damage proxy maps based on the method of coherent change detection (CCD) with Sentinel-1 SLC data, as described by `Tay *et al.* (2020) `_.
-
-The output data files consist of the damage proxy map as GeoTiff (*dmp_...tif*) and KMZ (*dmp_...kmz*) files, as well as the raw CCD values in GeoTiff (*CCD_...tif*) and GeoJSON (*CCD_...geojson*) formats.
-
-The files are found within a newly created folder. The folder name is based on the name of your AOI and the event date.
+The output data files consist of the damage proxy map as GeoTiff (*dmp_...tif*) and KMZ (*dmp_...kmz*) files, as well as the raw CCD values in GeoTiff (*CCD_...tif*) and GeoJSON (*CCD_...geojson*) formats. The files are found within a newly created folder. The folder name is based on the name of your AOI and the event date.
.. attention::
@@ -21,7 +18,7 @@ The two steps of the process are explained in the following section.
Select an AOI
-------------
-Using the provided AOI selector, select an AOI of your choice between the different methods available in the tool. We provide three administration descriptions (from Level 0 to Level 2) and three custom shapes (drawn directly on the map or OGR-compatible files).
+Using the provided **AOI selector**, select an AOI of your choice between the different methods available in the tool. We provide three administration descriptions (from Level 0 to Level 2) and three custom shapes (drawn directly on the map or OGR-compatible files).
.. figure:: https://raw.githubusercontent.com/sepal-contrib/damage_proxy_maps/main/doc/img/aoi_select.png
@@ -30,19 +27,17 @@ Using the provided AOI selector, select an AOI of your choice between the differ
Parameters
----------
-- Disaster event date: Pick the date where the disaster event happened.
-- Copernicus credentials: Provide your Sci-Hub credentials for search and download of the relevant Sentinel-1 scenes. If you do not have an account, register with `Copernicus Sci-Hub `_.
+- **Disaster event date**: Choose the date where the disaster event happened.
+- **Copernicus credentials**: Provide your Sci-Hub credentials for searching and downloading relevant Sentinel-1 scenes. If you do not have an account, register with `Copernicus Sci-Hub `_.
-Selecting this button will trigger the full workflow (Note: Some of the steps may take a while, such as downloading and processing, so if you have an unstable internet connection, set the minimum runtime of your instance to two hours; otherwise, stay connected to the SEPAL website by neither closing your browser or browser tab.)
+Selecting this button will trigger the full workflow (Note: Some of the steps may take a while, such as downloading and processing, so if you have an unstable internet connection, set the minimum runtime of your instance to two hours; otherwise, stay connected to the SEPAL website by neither closing your browser nor browser tab.)
.. note::
- If the processing does not finish, you can rerun the module with the same parameters, and the processing will continue from where it stopped.
+ If the processing does not finish, you can rerun the module with the same parameters and it will continue from where it stopped.
-Once the computation is finished, the result files will be stored in the :code:`module_results/Damage_proxy_map/_/` folder.
+Once the computation has finished, the result files will be stored in the :code:`module_results/Damage_proxy_map/_/` folder.
.. figure:: https://raw.githubusercontent.com/sepal-contrib/damage_proxy_maps/main/doc/img/complete.png
.. custom-edit:: https://raw.githubusercontent.com/sepal-contrib/damage_proxy_maps/release/doc/en.rst
-
-.. custom-edit:: https://raw.githubusercontent.com/sepal-contrib/damage_proxy_maps/release/doc/en.rst
diff --git a/docs/source/modules/dwn/fcdm.rst b/docs/source/modules/dwn/fcdm.rst
index b94e99c871..350686e86f 100644
--- a/docs/source/modules/dwn/fcdm.rst
+++ b/docs/source/modules/dwn/fcdm.rst
@@ -1,5 +1,6 @@
Forest Canopy Disturbance Monitoring (FCDM)
===========================================
+*Map canopy disturbances (natural- or human-induced) within (semi-)evergreen forests*
.. note::
@@ -11,11 +12,11 @@ FCDM tool
Overview
^^^^^^^^
-The **FCDM** tool supports the detection of forest canopy disturbance from remote sensing satellites, providing indications of forest degradation processes.
+The **Forest Canopy Disturbance Monitoring (FCDM)** tool supports the detection of forest canopy disturbance from remote sensing satellites, providing indications of forest degradation processes.
-Reporting on forest degradation is required by many tropical countries participating in the programme, Reducing Emissions from Deforestation and Forest Degradation and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries (REDD+). However, compared to deforestation, the mapping of forest degradation has proven to be much more challenging technically. In particular, signs of a forest canopy disturbance is less prominent, as it does not result in a change of land cover.
+Reporting on forest degradation is required by many tropical countries participating in the REDD+ programme (REDD+ refers to Reducing Emissions from Deforestation and Forest Degradation and the role of conservation, sustainable management of forests and enhancement of forest carbon stocks in developing countries). However, compared to deforestation, the mapping of forest degradation has proven to be much more challenging technically. In particular, signs of a forest canopy disturbance is less prominent, as it does not result in a change of land cover.
-The FCDM tool has been developed at the Joint Research Centre (JRC) within the ReCaREDD Project. It uses a change detection approach based on the difference (delta) of the self-referenced "Normalized Burn Ratio" index (Delta-rNBR) (`Langner et al. [2018] `_), in order to detect forest canopy change over defined periods at the pixel and sub-pixel level. The underlying Delta-rNBR index allows the detection of forest canopy disturbance within tropical (semi-)evergreen forest canopies ("forest remaining forest"), resulting from certain actions, such as tree removal, felling damages, logging trails, and leading.
+The **FCDM** tool has been developed at the Joint Research Centre (JRC) within the ReCaREDD Project. It uses a change detection approach based on the difference (delta) of the self-referenced "Normalized Burn Ratio" index (Delta-rNBR) (`Langner et al. [2018] `_), in order to detect forest canopy change over defined periods at the pixel and sub-pixel level. The underlying Delta-rNBR index allows the detection of forest canopy disturbance within tropical (semi-)evergreen forest canopies ("forest remaining forest"), resulting from certain actions (e.g. tree removal, felling damages, logging trails, and leading).
.. thumbnail:: https://forobs.jrc.ec.europa.eu/iforce/images/fcdm_process.jpg
:title: Processing steps of the FCDM tool
@@ -24,16 +25,16 @@ The FCDM tool has been developed at the Joint Research Centre (JRC) within the R
General purpose
^^^^^^^^^^^^^^^
-- detection of all kinds of tree canopy disturbances (natural or human-induced) within evergreen and semi-evergreen forests
-- manual screening of data by an experienced human interpreter in order to separate natural disturbances from human disturbances
-- near real-time monitoring of possible canopy cover changes
+- detection of all kinds of tree canopy disturbances (natural or human-induced) within evergreen and semi-evergreen forests;
+- manual screening of data by an experienced human interpreter in order to separate natural disturbances from human disturbances; and
+- near real-time (NRT) monitoring of possible canopy cover changes.
Citation
^^^^^^^^
Publications, models and data products that make use of this tool must include proper acknowledgement, including citing datasets and presenting the following reference for the source:
-- Langner, A., Miettinen, J., Kukkonen, M., Vancutsem, C., Simonetti, D., Vieilledent, G., Verhegghen, A., Gallego, J. & Stibig, H-J. 2018. Towards Operational Monitoring of Forest Canopy Disturbance in Evergreen Rain Forests: A Test Case in Continental Southeast Asia. *Remote Sensing* (10, 4): 544. https://doi.org/10.3390/rs10040544
+- Langner, A., Miettinen, J., Kukkonen, M., Vancutsem, C., Simonetti, D., Vieilledent, G., Verhegghen, A., Gallego, J. & Stibig, H-J. 2018. Towards Operational Monitoring of Forest Canopy Disturbance in Evergreen Rain Forests: A Test Case in Continental Southeast Asia. *Remote Sensing*, 10(4): 544. https://doi.org/10.3390/rs10040544
Contact
^^^^^^^
@@ -49,10 +50,10 @@ Contact
Usage
-----
-Select an area of interest (AOI)
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Select an area of interest
+^^^^^^^^^^^^^^^^^^^^^^^^^^
-*Delta-rNBR* will be calculated based on user inputs. The first mandatory input is the AOI. In this step, you’ll have the possibility to choose from a predefined list of administrative layers or use your own datasets. The available options are:
+*Delta-rNBR* will be calculated based on user inputs. The first mandatory input is the area of interest (AOI). In this step, you’ll have the possibility to choose from a pre-defined list of administrative layers or use your own datasets. The available options are:
**Pre-defined layers**
@@ -79,14 +80,14 @@ After choosing the desired area, select the :code:`Select these inputs` button;
Workflow parameters
^^^^^^^^^^^^^^^^^^^
-Select :guilabel:`process` to display the process panel. In this section, each parameter you can set in the app to customize your analysis will be described.
+Select :guilabel:`process` to display the **Process** pane. In this subsection, we will describe the parameters available in the app to customize your analysis.
Select time periods
*******************
Selected time periods are periods that will be used as **Reference** and **Analysis** periods.
-Use the :code:`datepicker` to select the start date and end date of these time periods.
+Use the :code:`datepicker` to select the start and end dates of these time periods.
.. thumbnail:: https://raw.githubusercontent.com/12rambau/fcdm/master/doc/img/datepicker-demo.gif
:title: Demo of datepicker usage
@@ -113,11 +114,11 @@ Sensors
The **Sensor** list is updated with the available satellite dataset for the selected time periods. The user is thus forced to select the dates first.
-Sensors can be selected in the dropdown menu. This list is only showing satellite datasets that are available for the selected time period. The user needs to select at least one.
+Sensors can be selected in the dropdown menu. This list is only showing satellite datasets that are available for the selected time period. Select at least one.
.. note::
- Data from Sentinel and Landsat programme cannot be mixed.
+ Data from Sentinel and Landsat programmes cannot be mixed.
.. thumbnail:: https://raw.githubusercontent.com/12rambau/fcdm/master/doc/img/sensor.png
:title: Select the Landsat family (L7 and L8) without thresholding L7 data
@@ -136,7 +137,7 @@ Value of the cloud buffering used in the cloud masking operation of the FCDM pro
Basemap
*******
-The FCDM process needs to create a forest/non-forest mask to produce results, which is derived from data provided by the user.
+The FCDM process needs to create a **Forest/non-forest** mask to produce results, which is derived from data provided by the user.
Three default datasets can be selected:
@@ -146,15 +147,15 @@ Three default datasets can be selected:
The year is automatically set to the start year of the **Reference** period.
-- **TMF**: This mask will be based on the `Tropical Moist Forest product from the JRC`. The user will also need to provide the year of analysis.
+- **TMF**: This mask will be based on the `Tropical Moist Forest product from the JRC `. The user will also need to provide the year of analysis.
.. tip::
- The year is automatically set to the start year of the **Reference** period.
+ The year is automatically set to the start year of the **Reference period**.
- **No forest map**: There will be no forest masking.
-The user can also use any GEE asset by setting it's value in the :code:`textfield` or selecting an image in the raster list. The image needs to be a mask with values of the first band set to:
+The user can also use any GEE asset by setting its value in the :code:`textfield` or selecting an image in the **Raster list**. The image needs to be a mask with values of the first band set to:
- 0 for non-forest
- 1 for forest
@@ -166,12 +167,14 @@ The user can also use any GEE asset by setting it's value in the :code:`textfiel
Advanced parameters
*******************
-These are the advanced parameters of the FCDM process. Please read this section carefully to understand their objectives.
+These are the advanced parameters of the FCDM process.
+
+Please read this section carefully to understand their objectives.
-Self referencing
+Self-referencing
################
-For the self-referencing kernel, set one parameter, **Radius of circular kernel**, which will define the buffer used for the self-referencing operation (in metres; by default, set to: code:`150`).
+For the self-referencing kernel, set one parameter – **Radius of circular kernel** – which will define the buffer used for the self-referencing operation (in metres; by default, set to: code:`150`).
DDR
###
@@ -195,12 +198,12 @@ Compute
Select :guilabel:`Run FCDM Computation` to launch the process in GEE. The layers will automatically be displayed on the visualization map.
-.. attention::
+.. note::
This operation takes very little time since the actual computation is done when the map refreshes itself.
.. thumbnail:: https://raw.githubusercontent.com/12rambau/fcdm/master/doc/img/run_fcdm.png
- :title: The run panel
+ :title: The **Run** pane
:group: fcdm
Map
@@ -209,7 +212,7 @@ Map
In this map, different layers of the computation will be displayed:
- the forest mask (in green)
-- the delta-rNBR (in red, where there are disturbances)
+- the Delta-rNBR (in red, where there are disturbances)
- the AOI (in light blue)
.. note::
@@ -218,7 +221,7 @@ In this map, different layers of the computation will be displayed:
.. attention::
- Every time the user zooms in, GEE will recompute all values on the fly. This operation is time consuming, so be patient. The forest mask is a simple image; when the delta-rNBR finishes refreshing, it will be perfectly aligned with the image. If it's blurry, GEE is still computing.
+ Every time the user zooms in, GEE will recompute all values on the fly. This operation is time consuming, so be patient. The forest mask is a simple image; when the Delta-rNBR finishes refreshing, it will be perfectly aligned with the image (if it's blurry, GEE is still computing).
.. thumbnail:: https://raw.githubusercontent.com/12rambau/fcdm/master/doc/img/result_map.png
:title: Vizualization of the Sandan province with all default parameters with the reference period of 2019 and 2020 analysis
@@ -227,7 +230,7 @@ In this map, different layers of the computation will be displayed:
Download images
^^^^^^^^^^^^^^^
-Select the **cloud** in the upper-left corner of the map to open the following pop-up window, where you will be able to customize exportation parameters.
+Select the cloud in the upper-left corner of the map to open the following pop-up window, where you will be able to customize exportation parameters.
.. thumbnail:: https://raw.githubusercontent.com/12rambau/fcdm/master/doc/img/export_panel.png
:title: The downloading pop-up window
@@ -237,8 +240,8 @@ Select the **cloud** in the upper-left corner of the map to open the following p
- **Filename prefix**: The prefix used to describe the file (in SEPAL) or asset (in GEE) (by default, :code:`__`__.
-
-.. custom-edit:: https://github.com/openforis/sepal-doc/blob/master/docs/source/_templates/no_module.rst
diff --git a/docs/source/modules/dwn/gfc_wrapper_R.rst b/docs/source/modules/dwn/gfc_wrapper_R.rst
index f84eb97374..51b7d75b89 100644
--- a/docs/source/modules/dwn/gfc_wrapper_R.rst
+++ b/docs/source/modules/dwn/gfc_wrapper_R.rst
@@ -1,12 +1,6 @@
gfc_wrapper_R
=============
-.. warning::
+.. note::
- The english documentation of the module have is under construction.
-
-.. tip::
-
- For specific help, please open an issue on our repository by clicking on this `link `__.
-
-.. custom-edit:: https://github.com/openforis/sepal-doc/blob/master/docs/source/_templates/no_module.rst
+ This article of SEPAL documentation is not yet available. To request that it be made available, use the `GitHub Issue Tracker `__.
diff --git a/docs/source/modules/dwn/gfc_wrapper_python.rst b/docs/source/modules/dwn/gfc_wrapper_python.rst
index f40d19a581..a94675ae20 100644
--- a/docs/source/modules/dwn/gfc_wrapper_python.rst
+++ b/docs/source/modules/dwn/gfc_wrapper_python.rst
@@ -1,26 +1,25 @@
Forest change mask
==================
-
*Base forest mask and fragmentation tool*
-This application allows users to:
+As a base forest mask and fragmentation tool, this application allows users to:
- define an area of interest (AOI);
- retrieve tree cover change data from the `Hansen et al. (2013) dataset `_; and
- combine layers to produce a forest change map for a given canopy cover threshold.
-Background information on global forest change (GFC)
-----------------------------------------------------
+Background information on Global Forest Change
+----------------------------------------------
-GFC provides global layers of information on tree cover and tree cover change since 2000, at 30 metre spatial resolution, consisting of:
+Global forest change (GFC) provides global layers of information on tree cover and tree cover change since 2000 (at 30 m spatial resolution), consisting of:
-- tree canopy cover for the year 2000 (treecover2000);
-- global forest cover gain for 2000–2012 (gain); and
-- year of gross forest cover loss event (lossyear).
+- tree canopy cover for the year 2000 (**treecover2000**);
+- global forest cover gain for 2000–2012 (**gain**); and
+- year of gross forest cover loss event (**lossyear**).
-For more information please refer to:
+For more information, please refer to:
-- Hansen, M.C. et al. 2013. High-Resolution Global Maps of 21st-Century Forest Cover Change. *Science* 342: 850–53. https://science.sciencemag.org/content/342/6160/850
+- Hansen, M.C. *et al.* 2013. High-Resolution Global Maps of 21st-Century Forest Cover Change. *Science*, 342: 850–53. https://science.sciencemag.org/content/342/6160/850
- University of Maryland, `Global forest change dataset `_
.. thumbnail:: https://earthengine.google.com/static/images/hansen.jpg
@@ -35,7 +34,9 @@ Usage
Select an AOI
^^^^^^^^^^^^^
-Using the provided **AOI selector**, choose an AOI of your choice between different methods available in the tool. We provide three administrative descriptors (from level 0 to 2) and three custom shapes (drawn directly on the map, asset or shapefile).
+Using the provided **AOI selector**, choose an AOI of your choice between different methods available in the tool.
+
+We provide three administrative descriptors (from level 0 to 2) and three custom shapes (drawn directly on the map, asset, or shapefile).
.. thumbnail:: https://raw.githubusercontent.com/openforis/gfc_wrapper_python/master/doc/img/select_aoi.png
:group: gfc_wrapper
@@ -50,10 +51,10 @@ GFC visualization
Two parameters are available to select the data:
-- Use the slider to change the threshold to consider between forest and non-forest areas. Once you've chosen a value, select :code:`update map` to update the interactive map layers.
+- Use the slider to change the threshold to consider (between forest and non-forest areas). Once you've chosen a value, select :code:`update map` to update the interactive map layers.
- Use the range slider to move the dates to consider in the analysis.
-The new layer is a combination of GFC layers to produce a forest change map for a given canopy cover threshold. Only pixels that have tree cover above the threshold will be considered forest. Every tree-covered pixel prior to the start date will be considered as **non forest** and every changed that occurs after the end date will be considered **stable forest**. The legend is displayed in the map. You're allowed to zoom in and out; the data will be dynamically recomputed in GEE.
+The new layer is a combination of GFC layers to produce a forest change map for a given canopy cover threshold. Only pixels that have tree cover above the threshold will be considered forest. Every tree-covered pixel prior to the start date will be considered as **non-forest** and every changed that occurs after the end date will be considered **stable forest**. The legend is displayed in the map. You're allowed to zoom in and out; the data will be recomputed dynamically in GEE.
When changing the value of the threshold or the dates, a new layer will be added to the map, so you can compare and select the most appropriate parameters for your analysis.
@@ -75,7 +76,9 @@ Three results will be produced:
- the distribution of each defined zone (:code:`..._gfc_stat.csv`); and
- the legend of the raster (:code:`..._gfc_legend.pd```).
-You can download these three files directly from the interface using the green buttons. The files are named after your parameters, following this convention: :code:`___.`
+Download these three files directly from the interface using the green buttons.
+
+The files are named after your parameters, following this convention: :code:`___.`
.. attention::
diff --git a/docs/source/modules/dwn/gwb.rst b/docs/source/modules/dwn/gwb.rst
index 723ee7da9b..260a304e7d 100644
--- a/docs/source/modules/dwn/gwb.rst
+++ b/docs/source/modules/dwn/gwb.rst
@@ -1,27 +1,26 @@
-GuidosToolbox Workbench (GWB)
-=============================
+GWB
+===
+*Suite of various geospatial image analysis tools*
-This article of SEPAL documentation provides usage instructions for the image analysis module **GWB** (`GuidosToolbox Workbench `_) implemented as a Jupyter dashboard on the SEPAL platform.
-
-Citation reference: `GuidosToolbox Workbench: Spatial analysis of raster maps for ecological applications `_.
+This article provides usage instructions for the image analysis module **GuidosToolbox Workbench (GWB)**, implemented as a Jupyter dashboard on the SEPAL platform (for more information on GWB), see ``_).
Introduction
------------
-In 2008, `GuidosToolbox (`GTB `_) (`Vogt and Riitters, 2017 `_) was developed as a graphical user interface (GUI) to the Morphological Spatial Pattern Analysis `(MSPA `_) of raster data (`Soille and Vogt, 2009 `_).
+In 2008, `GuidosToolbox (GTB) `_) (Vogt and Riitters, 2017) was developed as a graphical user interface (GUI) to the `Morphological Spatial Pattern Analysis (MSPA) `_ of raster data (Soille and Vogt, 2009).
-GTB has since been enhanced with numerous modules for analysis of landscape objects, patterns, and networks, as well as specialized modules for assessing fragmentation and restoration.
+GTB has since been enhanced with numerous modules for analysis of landscape objects, patterns and networks, as well as specialized modules for assessing fragmentation and restoration.
-GWB provides the most popular GTB modules as a set of command-line applications for 64bit Linux systems.
+**GWB** provides the most popular GTB modules as a set of command-line applications for 64bit Linux systems.
-In this article, we describe the implementation of GWB on the SEPAL platform as a Jupyter dashboard based on the `GWB CLI tool `_.
+In this article, we describe the implementation of **GWB** on the SEPAL platform as a Jupyter dashboard based on the `GWB CLI tool `_.
Presentation
^^^^^^^^^^^^
To launch the app, `register to SEPAL `_.
-Then, navigate to the SEPAL **Apps** dashboard (purple wrench icon in the left panel).
+Then, navigate to the SEPAL **Apps** dashboard (purple wrench icon in the left pane).
Finally, search for and select **GWB ANALYSIS**.
@@ -29,7 +28,9 @@ Finally, search for and select **GWB ANALYSIS**.
:title: SEPAL dashboard
:group: gwb-module
-The application should launch itself and display the **About** section. Select the tool you want to use.
+The application should launch itself and display the **About** section.
+
+Select the tool you want to use.
.. note::
@@ -40,7 +41,7 @@ The application should launch itself and display the **About** section. Select t
:group: gwb-module
This licence needs to be accepted to use the **GWB** modules. It is also available in the :code:`Licence` section of the app.
- If you don't want to accept the licence, close the **App** tab.
+ If you don't want to accept the licence, close the app tab.
Usage
^^^^^
@@ -50,11 +51,11 @@ General structure
The application is structured as follows:
-On the left side you will find a navigation drawer that you can open and close using the :btn:`` (upper-left side of the window).
+On the left, there is a navigation drawer that you can open and close using the :btn:`` (upper-left side of the window).
.. tip::
- On small devices such as tablets or phones, the navigation drawer will be hidden by default. Select the :btn:`` (upper-left side of the window) to display the full extent of the app.
+ On small devices such as tablets or phones, the navigation drawer will be hidden by default. Select the :btn:`` (upper-left side) to display the full extent of the app.
.. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/small_device_without_menu.png
:title: Small screen without drawer
@@ -66,7 +67,7 @@ On the left side you will find a navigation drawer that you can open and close u
:width: 49%
:group: gwb-module
-Each name in the list corresponds to one **GWB** module, presented separately in the following sections. By selecting a name, the panels relative to the function will be displayed.
+Each name in the list corresponds to one **GWB** module, presented separately in the following sections. By selecting a name, the panes relative to the function will be displayed.
.. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/landing.png
:title: Presentation of the structure
@@ -92,7 +93,7 @@ In the next section, each module and its specificities will be described.
Modules
-------
-Each module is presented individually in this article. You can directly jump to the module of interest by selecting the related link under the **Modules** section in the right panel of this page – the documentation will guide you through the respective processing steps.
+Each module is presented individually in this article. You can directly jump to the module of interest by selecting the related link under the **Modules** section in the right pane of this page – the documentation will guide you through the respective processing steps.
Accounting (ACC)
^^^^^^^^^^^^^^^^
@@ -104,7 +105,7 @@ Set up the input image
.. tip::
- You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second panel to add the following files to your :code:`downloads` folder:
+ You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second pane to add the following files to your :code:`downloads` folder:
- :code:`example.tif`: 0 bytes - Missing, 1 byte - Background, 2 bytes - Foreground
- :code:`clc3class.tif`: 1 byte - Agriculture, 2 bytes - Natural, 3 bytes - Developed
@@ -115,7 +116,7 @@ Set up the input image
Once the files are downloaded, follow the normal process using the :code:`downloads/example.tif` file (two classes).
-The first step requires reclassifying your image. Using the **Reclassifying** panel, select your image in your SEPAL folder.
+The first step requires reclassifying your image. Using the **Reclassifying** pane, select your image in your **SEPAL folders**.
.. attention::
@@ -125,10 +126,10 @@ The dropdown menus will list the discrete values of your raster input image.
Select each class in your image and place them in one of the following categories:
-- background
-- foreground
-- special background 1 (optional)
-- special background 2 (optional)
+- **background**
+- **foreground**
+- **special background 1** (optional)
+- **special background 2** (optional)
Every class that is not set to a reclassifying category will be considered "missing data" (0 byte).
@@ -200,7 +201,7 @@ Two options are available:
Run the analysis
""""""""""""""""
-Once your parameters are set, launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn green and display the computation log.
+Once your parameters are set, launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn green and display the **Computation log**.
.. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/acc_results.png
:title: Information logs
@@ -215,7 +216,7 @@ The resulting files are stored in the folder :code:`module_results/gwb/acc/`. Fo
.. attention::
- If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance, and run your analysis again.
+ If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance and run your analysis again.
Here is the result of the computation using the default parameters on the :code:`example.tif` file.
@@ -236,7 +237,7 @@ Set up the input image
.. tip::
- You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second panel to add the following files to your :code:`downloads` folder:
+ You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second pane to add the following files to your :code:`downloads` folder:
- :code:`example.tif`: 0 bytes - Missing, 1 byte - Background, 2 bytes - Foreground
- :code:`clc3class.tif`: 1 byte - Agriculture, 2 bytes - Natural, 3 bytes - Developed
@@ -247,7 +248,7 @@ Set up the input image
Once the files are downloaded, follow the normal process using the :code:`downloads/example.tif` file (two classes).
-The first step requires reclassifying your image. Using the **Reclassifying** panel, select the image in your **SEPAL folder**.
+The first step requires reclassifying your image. Using the **Reclassifying** pane, select the image in your **SEPAL folder**.
.. attention::
@@ -255,8 +256,8 @@ The first step requires reclassifying your image. Using the **Reclassifying** pa
The dropdown menus will list the discrete values of your raster input image. Select each class in your image and place them in one of the following categories:
-- background
-- foreground
+- **background**
+- **foreground**
Every class that is not set to a reclassifying category will be considered "missing data" (0 bytes).
@@ -286,7 +287,7 @@ You will need to select parameters for your computation:
Foreground connectivity
#######################
-This sets the foreground connectivity of your analysis. Specifically,
+This sets the foreground connectivity of your analysis. Specifically:
- 8 neighbors (default) will use every pixel in the vicinity (including diagonals)
- 4 neighbors will only use the vertical and horizontal one
@@ -323,7 +324,7 @@ The resulting files are stored in the folder :code:`module_results/gwb/dist/`. F
.. attention::
- If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance, and run your analysis again.
+ If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance and run your analysis again.
Here is the result of the computation using the default parameters on the :code:`example.tif` file.
@@ -338,20 +339,20 @@ Here is the result of the computation using the default parameters on the :code:
Forest area density (FAD)
^^^^^^^^^^^^^^^^^^^^^^^^^
-This module will conduct the **fragmentation** analysis at **five fixed observation scales**.
+This module will conduct the **Fragmentation** analysis at **five fixed observation scales**.
-Since forest fragmentation is scale-dependent, fragmentation is reported at five observation scales, allowing different observers to make their own choice about scales and threshold of concern.
+Since forest fragmentation is scale-dependent, fragmentation is reported at five observation scales, allowing different observers to make their own choice about scales and threshold of concern.
The change of fragmentation across different observation scales provides additional information of interest.
-Fragmentation is measured by determining forest area density (**FAD**) within a shifting, local neighbourhood. It can be measured at pixel or patch level. The results are spatially explicit maps and tabular summary statistics. Details on the methodology and input/output options can be found in the `Fragmentation product sheet `_.
+Fragmentation is measured by determining forest area density (**FAD**) within a shifting, local neighbourhood. It can be measured at pixel or patch level. The results are spatially explicit maps and tabular summary statistics (details on the methodology and input/output options can be found in the `Fragmentation product sheet `_).
Set up the input image
""""""""""""""""""""""
.. tip::
- You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second panel, which will add the following files to your :code:`downloads` folder:
+ You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second pane, which will add the following files to your :code:`downloads` folder:
- :code:`example.tif`: 0 bytes - Missing, 1 byte - Background, 2 bytes - Foreground
- :code:`clc3class.tif`: 1 byte - Agriculture, 2 bytes - Natural, 3 bytes - Developed
@@ -362,7 +363,7 @@ Set up the input image
Once the files are downloaded, follow the normal process using the :code:`downloads/example.tif` file (two classes).
-The first step requires reclassifying your image. Using the **Reclassifying** panel, select the image in your **SEPAL folder**.
+The first step requires reclassifying your image. Using the **Reclassifying** pane, select the image in your **SEPAL folders**.
.. attention::
@@ -428,14 +429,14 @@ Options
Three computation options are available:
-- FAD: per-pixel density, color-coded into 6 fragmentation classes
-- FAD-APP2: average per-patch density, color-coded into 2 classes
-- FAD-APP5: average per-patch density, color-coded into 5 classes
+- **FAD**: per-pixel density, color-coded into 6 fragmentation classes
+- **FAD-APP2**: average per-patch density, color-coded into 2 classes
+- **FAD-APP5**: average per-patch density, color-coded into 5 classes
Run the analysis
""""""""""""""""
-Once your parameters are all set you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn green and display the computation log.
+Once your parameters are all set, you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn green and display the **Computation** log.
.. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/fad_results.png
:title: Information logs
@@ -468,14 +469,16 @@ Here is the result of the computation using the default parameters on the :code:
Fragmentation (FRAG)
^^^^^^^^^^^^^^^^^^^^
-This module will conduct the **Fragmentation** analysis at a **user-selected observation scale**. This module and its option are similar to :code:`fad`, but allow the user to specify a single (or multiple) specific observation scale. The results are spatially explicit maps and tabular summary statistics. Details on the methodology and input/output options can be found in the `Fragmentation product sheet `_.
+This module will conduct the **Fragmentation** analysis at a **user-selected observation scale**.
+
+This module and its option are similar to :code:`fad`, but allow the user to specify a single (or multiple) specific observation scale. The results are spatially explicit maps and tabular summary statistics. Details on the methodology and input/output options can be found in the `Fragmentation product sheet `_.
Set up the input image
""""""""""""""""""""""
.. tip::
- You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second panel, which will add the following files to your :code:`downloads` folder:
+ You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second pane, which will add the following files to your :code:`downloads` folder:
- :code:`example.tif`: 0 bytes - Missing, 1 byte - Background, 2 bytes - Foreground
- :code:`clc3class.tif`: 1 byte - Agriculture, 2 bytes - Natural, 3 bytes - Developed
@@ -486,7 +489,7 @@ Set up the input image
Once the files are downloaded, follow the normal process using the :code:`downloads/example.tif` file (two classes).
-The first step requires reclassifying your image. Using the **Reclassifying** panel, select the image in your **SEPAL folder**.
+The first step requires reclassifying your image. Using the **Reclassifying** pane, select the image in your **SEPAL folders**.
.. attention::
@@ -530,7 +533,7 @@ You will need to select parameters for your computation:
- Spatial pixel resolution: 25
- Computation precision: float-precision
- Window size: 23
- - Options: fragmentation at pixel or at patch level with various number of color-coded classes
+ - Options: fragmentation at pixel- or patch- level with various number of color-coded classes
Foreground connectivity
#######################
@@ -568,7 +571,7 @@ Four computation options are available:
Run the analysis
""""""""""""""""
-Once your parameters are all set, you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn green and display the computation log.
+Once your parameters are all set, you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn green and display the **Computation** log.
.. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/frag_results.png
:title: Information logs
@@ -584,9 +587,9 @@ The resulting files are stored in the folder :code:`module_results/gwb/frag/`. F
.. attention::
- If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance, and run your analysis again.
+ If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance and run your analysis again.
-Here is the result of the computation using the FAD-APP2 option on the :code:`example.tif` file:
+Here is the result of the computation using the **FAD-APP2** option on the :code:`example.tif` file:
.. thumbnail:: https://raw.githubusercontent.com/openforis/sepal-doc/master/docs/source/img/cli/gwb/example_fad-app2_23.png
:width: 50%
@@ -606,7 +609,7 @@ Set up the input image
.. tip::
- You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second panel, which will add the following files to your :code:`downloads` folder:
+ You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second pane, which will add the following files to your :code:`downloads` folder:
- :code:`example.tif`: 0 bytes - Missing, 1 byte - Background, 2 bytes - Foreground
- :code:`clc3class.tif`: 1 byte - Agriculture, 2 bytes - Natural, 3 bytes - Developed
@@ -617,7 +620,7 @@ Set up the input image
Once the files are downloaded, follow the normal process using the :code:`downloads/clc3class.tif` file (three classes).
-The first step requires reclassifying your image. Using the **Reclassifying** panel, select the image in your **SEPAL folder**.
+The first step requires reclassifying your image. Using the **Reclassifying** pane, select the image in your **SEPAL folders**.
.. attention::
@@ -659,7 +662,7 @@ Set the square window size (in pixels). It should be an odd number in [3, 5, ...
obs_scale = (pixres * kdim)^2 / 10000
-with
+Also note the following:
- :math:`obs_scale` in hectares
- :math:`pixres` in metres
@@ -668,7 +671,7 @@ with
Run the analysis
""""""""""""""""
-Once your parameters are all set, you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn green and display the computation log.
+Once your parameters are all set, you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn green and display the **Computation log**.
.. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/lm_results.png
:title: Information logs
@@ -687,7 +690,7 @@ The resulting files are stored in the folder :code:`module_results/gwb/lm/`. For
.. attention::
- If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance, and run your analysis again.
+ If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance and run your analysis again.
Here is the result of the computation using the default parameters on the :code:`clc3classes.tif` file:
@@ -713,7 +716,7 @@ Set up the input image
.. tip::
- You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second panel, which will add the following files to your :code:`downloads` folder:
+ You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second pane, which will add the following files to your :code:`downloads` folder:
- :code:`example.tif`: 0 byte - Missing, 1 byte - Background, 2 bytes - Foreground
- :code:`clc3class.tif`: 1 byte - Agriculture, 2 bytes - Natural, 3 bytes - Developed
@@ -724,11 +727,11 @@ Set up the input image
Once the files are downloaded, follow the normal process using the :code:`downloads/example.tif` file (two classes).
-The first step requires reclassifying your image. Using the **Reclassifying** panel, select the image in your **SEPAL folder**.
+The first step requires reclassifying your image. Using the **Reclassifying** pane, select the image in your **SEPAL folders**.
.. attention::
- If the image is on your local computer and not in your SEPAL folders, see `Exchange files with SEPAL `_.
+ If the image is on your local computer and not in your **SEPAL folders**, see `Exchange files with SEPAL `_.
The dropdown menus will list the discrete values of your raster input image. Select each class in your image and place them in one of the following categories:
@@ -780,7 +783,7 @@ This sets the foreground connectivity of your analysis:
Edge width
##########
-Define the width (measured in pixels) of the core-boundaries (Edges and Perforations).
+Define the width (measured in pixels) of the core-boundaries (edges and perforations).
Transition
##########
@@ -819,7 +822,7 @@ The resulting files are stored in the folder :code:`module_results/gwb/mspa/`. F
.. attention::
- If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance, and run your analysis again.
+ If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance and run your analysis again.
Here is the result of the computation using the default parameters on the :code:`example.tif` file.
@@ -834,18 +837,18 @@ Here is the result of the computation using the default parameters on the :code:
Density, Contagion or Adjacency Analysis (P223)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-This module will conduct the **Density** (P2), **Contagion** (P22) or **Adjacency** (P23) analysis of foreground (**FG**) objects at a user-selected observation scale (`Riitters et al., 2000 `_).
+This module will conduct the **Density** (P2), **Contagion** (P22) or **Adjacency** (P23) analysis of foreground (**FG**) objects at a user-selected observation scale (Riitters *et al.*, 2000).
The results are spatially explicit maps and tabular summary statistics.
-The classification is determined by measurements of forest amount (P2) and connectivity (P22) within the neighbourhood that is centred on a subject forest pixel. P2 is the probability that a pixel in the neighborhood is forest; P22 is the probability that a pixel next to a forest pixel is also forest.
+The classification is determined by measurements of forest amount (P2) and connectivity (P22) within the neighbourhood that is centred on a subject forest pixel. P2 is the probability that a pixel in the neighbourhood is forest; P22 is the probability that a pixel next to a forest pixel is also forest.
Set up the input image
""""""""""""""""""""""
.. tip::
- You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second panel, which will add the following files to your :code:`downloads` folder:
+ You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second pane, which will add the following files to your :code:`downloads` folder:
- :code:`example.tif`: 0 byte - Missing, 1 byte - Background, 2 bytes - Foreground
- :code:`clc3class.tif`: 1 byte - Agriculture, 2 bytes - Natural, 3 bytes - Developed
@@ -856,11 +859,11 @@ Set up the input image
Once the files are downloaded, follow the normal process using the :code:`downloads/example.tif` file (two classes).
-The first step requires reclassifying your image. Using the **Reclassifying** panel, select the image in your **SEPAL folder**.
+The first step requires reclassifying your image. Using the **Reclassifying** pane, select the image in your **SEPAL folders**.
.. attention::
- If the image is on your local computer but not in your **SEPAL folders**, consider reading `Exchange files with SEPAL `_.
+ If the image is on your local computer but not in your **SEPAL folders**, see `Exchange files with SEPAL `_.
The dropdown menus will list the discrete values of your raster input image. Select each class in your image and place them in one of the following categories:
@@ -904,7 +907,7 @@ Set the square window size (in pixels). It should be an odd number in [3, 5, ...
obs_scale = (pixres * kdim)^2 / 10000
-with
+Also note that:
- :math:`obs_scale` in hectares
- :math:`pixres` in metres
@@ -918,9 +921,9 @@ Set the precision used to compute your image. **Float precision** (default) will
Algorithm
#########
-The **P223** module can run: **FG-Density** (P2), **FG-Contagion** (P22), or **FG-Adjacency** (P23)
+The **P223** module can run: **FG-Density** (P2), **FG-Contagion** (P22), or **FG-Adjacency** (P23).
-**P223** will provide a color-coded image showing [0,100]% for either **FG-Density**, **FG-Contagion**, or **FG-Adjacency** masked for the foreground cover. Use the alternative options to obtain the original spatcon output without normalization, masking, or color-coding.
+**P223** will provide a color-coded image showing [0,100]% for either **FG-Density**, **FG-Contagion**, or **FG-Adjacency** masked for the foreground cover. Use the alternative options to obtain the original spatcon output without normalization, masking or color-coding.
.. tip::
@@ -948,7 +951,7 @@ The options are displayed with the following names in the dropdown menu:
Run the analysis
""""""""""""""""
-Once your parameters are set, you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn green and display the computation log.
+Once your parameters are set, you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn green and display the **Computation log**.
.. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/p223_results.png
:title: Information logs
@@ -962,7 +965,7 @@ The resulting files are stored in the folder :code:`module_results/gwb/p223/`. F
.. attention::
- If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance, and run your analysis again.
+ If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance and run your analysis again.
Here is the result of the computation using the P2 (Foreground-Density) option on the :code:`example.tif` file.
@@ -973,7 +976,7 @@ Here is the result of the computation using the P2 (Foreground-Density) option o
Parcellation (PARC)
^^^^^^^^^^^^^^^^^^^
-This module will conduct the **Parcellation** analysis, providing a statistical summary file (.txt/.csv format) with details for each unique class found in the image, as well as the full image content: class value, total number of objects, total area, and degree of parcellation.
+This module will conduct the **Parcellation** analysis, providing a statistical summary file (.txt/.csv format) with details for each unique class found in the image, as well as the full image content: class value, total number of objects, total area and degree of parcellation.
Details on the methodology and input/output options can be found in the `Parcellation product sheet `_.
@@ -982,7 +985,7 @@ Set up the input image
.. tip::
- You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second panel, which will add following files to your :code:`downloads` folder:
+ You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second pane, which will add the following files to your :code:`downloads` folder:
- :code:`example.tif`: 0 bytes - Missing, 1 byte - Background, 2 bytes - Foreground
- :code:`clc3class.tif`: 1 byte - Agriculture, 2 bytes - Natural, 3 bytes - Developed
@@ -993,7 +996,7 @@ Set up the input image
Once the files are downloaded, follow the normal process using the :code:`downloads/clc3classes.tif` file (three classes).
-The first step requires selecting your image in your **SEPAL folder**. The image must be a categorical .tif raster.
+The first step requires selecting your image in your **SEPAL folders**. The image must be a categorical .tif raster.
.. attention::
@@ -1033,7 +1036,7 @@ This sets the foreground connectivity of your analysis:
Run the analysis
""""""""""""""""
-Once your parameters are all set, you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn green and display the computation log.
+Once your parameters are all set, you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn green and display the **Computation** log.
.. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/parc_results.png
:title: Information logs
@@ -1047,7 +1050,7 @@ The resulting files are stored in the folder :code:`module_results/gwb/parc/`. F
.. attention::
- If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance, and run your analysis again.
+ If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance and run your analysis again.
Here is the result of the computation using the default parameters on the :code:`clc3classes.tif` file:
@@ -1073,7 +1076,7 @@ Set up the input image
.. tip::
- You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second panel, which will add the following files to your :code:`downloads` folder:
+ You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second pane, which will add the following files to your :code:`downloads` folder:
- :code:`example.tif`: 0 byte - Missing, 1 byte - Background, 2 bytes - Foreground
- :code:`clc3class.tif`: 1 byte - Agriculture, 2 bytes - Natural, 3 bytes - Developed
@@ -1084,7 +1087,7 @@ Set up the input image
Once the files are downloaded, follow the normal process using the :code:`downloads/example.tif` file (two classes).
-The first step requires reclassifying your image. Using the **Reclassifying** panel, select the image in your **SEPAL folder**.
+The first step requires reclassifying your image. Using the **Reclassifying** pane, select the image in your **SEPAL folders**.
.. attention::
@@ -1149,19 +1152,19 @@ The resulting files are stored in the folder :code:`module_results/gwb/rss/`. Fo
.. attention::
- If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance, and run your analysis again.
+ If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance and run your analysis again.
Here is the result of the computation using the default parameters on the :code:`example.tif` file:
.. csv-table::
- :header: FNAME, AREA, RAC[%], NR_OBJ, LARG_OBJ, APS, CNOA, ECA, COH[%], REST_POT[%]
-
- example_bin_map.tif,428490.00,42.860572,2850,214811,150.34737,311712,221292.76,51.644789,48.355211
+ :header: FNAME, AREA, RAC[%], NR_OBJ, LARG_OBJ, APS, CNOA, ECA, COH[%], REST_POT[%]
+
+ example_bin_map.tif,428490.00,42.860572,2850,214811,150.34737,311712,221292.76,51.644789,48.355211
Simplified pattern analysis (SPA)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-This module will conduct the **Simplified pattern analysis**, which shapes and conducts a segmentation of foreground patches into two, three, five, or six feature classes.
+This module will conduct the **Simplified pattern analysis**, which shapes and conducts a segmentation of foreground patches into two, three, five or six feature classes.
The results are spatially explicit maps and tabular summary statistics.
@@ -1174,7 +1177,7 @@ Set up the input image
.. tip::
- You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second panel, which will add the following files to your :code:`downloads` folder:
+ You can use the default dataset to test the module. Select the :code:`Download test dataset` button on the top of the second pane, which will add the following files to your :code:`downloads` folder:
- :code:`example.tif`: 0 bytes - Missing, 1 byte - Background, 2 bytes - Foreground
- :code:`clc3class.tif`: 1 byte - Agriculture, 2 bytes - Natural, 3 bytes - Developed
@@ -1185,7 +1188,7 @@ Set up the input image
Once the files are downloaded, follow the normal process using the :code:`downloads/example.tif` file (two classes).
-The first step requires reclassifying your image. Using the **Reclassifying** panel, select the image in your **SEPAL folder**.
+The first step requires reclassifying your image. Using the **Reclassifying** pane, select the image in your **SEPAL folders**.
.. attention::
@@ -1234,7 +1237,7 @@ Set the number of pattern classes you want to compute:
Run the analysis
""""""""""""""""
-Once your parameters are set, you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn green and display the computation log.
+Once your parameters are set, you can launch the analysis. The blue rectangle will display information about the computation. Upon completion, it will turn green and display the **Computation** log.
.. thumbnail:: https://raw.githubusercontent.com/12rambau/gwb/master/doc/img/spa_results.png
:title: Information logs
@@ -1248,7 +1251,7 @@ The resulting files are stored in the folder :code:`module_results/gwb/spa/`. Fo
.. attention::
- If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance, and run your analysis again.
+ If the rectangle turns red, carefully read the information in the log. For example, your current instance may be too small to handle the file you want to analyse. In this case, close the app, open a bigger instance and run your analysis again.
Here is the result of the computation using SPA2 (two classes) on the :code:`example.tif` file:
@@ -1256,4 +1259,11 @@ Here is the result of the computation using SPA2 (two classes) on the :code:`exa
:width: 50%
:group: gwb-module
+
+
+References
+----------
+
+`GuidosToolbox Workbench: Spatial analysis of raster maps for ecological applications `_
+
.. custom-edit:: https://raw.githubusercontent.com/sepal-contrib/gwb/release/doc/en.rst
diff --git a/docs/source/modules/dwn/gwl_analysis.rst b/docs/source/modules/dwn/gwl_analysis.rst
deleted file mode 100644
index 1fae0a6564..0000000000
--- a/docs/source/modules/dwn/gwl_analysis.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-gwl_analysis
-============
-
-.. warning::
-
- The english documentation of the module have is under construction.
-
-.. tip::
-
- For specific help, please open an issue on our repository by clicking on this `link `__.
-
-.. custom-edit:: https://github.com/openforis/sepal-doc/blob/master/docs/source/_templates/no_module.rst
diff --git a/docs/source/modules/dwn/planet_order.rst b/docs/source/modules/dwn/planet_order.rst
index a5ef7355e7..738036b807 100644
--- a/docs/source/modules/dwn/planet_order.rst
+++ b/docs/source/modules/dwn/planet_order.rst
@@ -1,5 +1,6 @@
Planet order
============
+*Order imagery from Planet Lab mosaics*
This dasboard application, based on the `SEPAL-UI `_ framework, provides users with an interface to explore and download Planet Lab images.
@@ -8,13 +9,13 @@ This dasboard application, based on the `SEPAL-UI `_) and (`connect your GEE account to SEPAL `_).
+ To start this module, `sign up for NICFI `_ and `connect your GEE account to SEPAL `_.
On the landing page of our module, in the lower left of the window, you will find three buttons:
-- :btn:``: Opens the GitHub repository that is used to create this module in a new tab (our code is open-source and distributed under the MIT license).
-- :btn:``: Leads to the wiki page.
-- :btn:``: Opens the issue tracker of our GitHub repository in a new tab (you can write here if you experience bugs or if you have any feature requests; the SEPAL team will answer as quickly as possible).
+- :btn:``: Opens the **GitHub repository** that is used to create this module in a new tab (our code is open-source and distributed under the MIT license).
+- :btn:``: Leads to the **Wiki page**.
+- :btn:``: Opens the **Issue tracker** of our GitHub repository in a new tab (you can write here if you experience bugs or if you have any feature requests; the SEPAL team will answer as quickly as possible).
Parameters
----------
@@ -26,19 +27,19 @@ In the lower-right corner, two tabs are available:
In the upper-left corner, three other tabs will allow you to interact with data:
-- :btn:``: the Planet order manager
-- :btn:``: color management
-- :btn:``: download panel
+- :btn:``: the **Planet order manager**
+- :btn:``: **Colour management**
+- :btn:``: **Download** pane
Select an AOI
-------------
-The application uses the same AOI selector that you will find in many other SEPAL applications (see the `Module AOI section `__ for more information).
+The application uses the same **AOI selector** that you will find in many other SEPAL applications (for more information, see the `Module AOI section `__).
Planet Lab authentication
-------------------------
-To authenticate the user, complete the form in the second tab with either your credentials or a Planet API key, which you can find in the settings section in your Planet profile page. Once the values are set, select :btn:`Validate`. If your Planet credentials are valid, the :btn:`NICFI` button should turn green. You can now select a mosaic from the provided list.
+To authenticate the user, complete the form in the second tab with either your credentials or a Planet API key, which you can find in the **Settings** section in your **Planet profile page**. Once the values are set, select :btn:`Validate`. If your Planet credentials are valid, the :btn:`NICFI` button should turn green. You can now select a mosaic from the provided list.
.. thumbnail:: https://raw.githubusercontent.com/12rambau/planet-order/master/doc/img/planet_auth.png
:group: planet-order
@@ -60,16 +61,16 @@ Once a mosaic is selected, the module will display basemaps on the map.
Manage color combination
------------------------
-Select :btn:`` on the upper-left side of the map, which will show the different color combinations available, including:
+Select :btn:`` on the upper-left side of the map, which will show the different colour combinations available, including:
-- Red-green-blue (RGB)
-- Color-infrared (CIR)
-- Normalized difference vegetation index (NDVI)
-- Normalized difference water index (NDWI)
-- Visual atmosphere resistance index (VARI)
-- Modified soil-adjusted vegetation index (MSAVI2)
-- Modified triangular vegetation index (MTVI2)
-- Triangular greenness index (TGI)
+- Red-green-blue (**RGB**)
+- Color-infrared (**CIR**)
+- Normalized difference vegetation index (**NDVI**)
+- Normalized difference water index (**NDWI**)
+- Visual atmosphere resistance index (**VARI**)
+- Modified soil-adjusted vegetation index (**MSAVI2**)
+- Modified triangular vegetation index (**MTVI2**)
+- Triangular greenness index (**TGI**)
Selecting one will update the displayed basemap.
diff --git a/docs/source/modules/dwn/plot_generator.rst b/docs/source/modules/dwn/plot_generator.rst
deleted file mode 100644
index 7c0efd50b0..0000000000
--- a/docs/source/modules/dwn/plot_generator.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-plot_generator
-==============
-
-.. warning::
-
- The english documentation of the module have is under construction.
-
-.. tip::
-
- For specific help, please open an issue on our repository by clicking on this `link `__.
-
-.. custom-edit:: https://github.com/openforis/sepal-doc/blob/master/docs/source/_templates/no_module.rst
diff --git a/docs/source/modules/dwn/sae_analysis.rst b/docs/source/modules/dwn/sae_analysis.rst
index 3510a2033e..4ba7233e22 100644
--- a/docs/source/modules/dwn/sae_analysis.rst
+++ b/docs/source/modules/dwn/sae_analysis.rst
@@ -1,5 +1,5 @@
-Stratified Area Estimation (SAE) – Analysis
-===========================================
+SAE analysis
+============
Analyse results from a stratified sampling design for area estimates
--------------------------------------------------------------------
@@ -8,12 +8,12 @@ The aim of this stratified sampling design tool is to analyse results from a str
The concept is derived from map accuracy assessment principles. Characterized frequency of errors (i.e. omission and commission) for each map class may be used to compute area estimates and estimate the uncertainties (i.e. confidence intervals) for the areas on each class.
-First, open the tool by selecting **Stratified Area Estimator - Analysis** in the **Apps** window.
+First, open the tool by selecting **Stratified Area Estimator (SAE) - Analysis** in the **Apps** window.
-You will land on the **Introduction** page which allows you to choose your language and provides background information on the tool. Note that **Reference and Documents** are in the same place as the **Design** tool. The pages that contain the necessary steps for the workflow are on the left side of the screen and need to be completed sequentially.
+You will land on the **Introduction** page which allows you to choose your language and provides background information on the tool. Note that **Reference and documents** are in the same place as the **Design** tool. The pages that contain the necessary steps for the workflow are on the left side of the screen and need to be completed sequentially.
.. figure:: https://raw.githubusercontent.com/openforis/accuracy-assessment/master/aa_analysis/img/stratified_estimator_analysis_tool.png
- :alt: The stratified estimator analysis tool.
+ :alt: The SAE tool
:align: center
Select the **Inputs** page on the left side of the screen. You will see two data requirements under the **Select input files** section.
@@ -23,10 +23,10 @@ Select the **Inputs** page on the left side of the screen. You will see two data
- For projects completed in Collect Earth Online (CEO): Select the **Reference data** button and navigate to the .csv file you downloaded from CEO and uploaded to SEPAL.
- For projects completed in the CEO-SEPAL bridge:
- Check that you are logged out of the CEO website.
- - Paste the URL from your CEO-SEPAL bridge project into the field marked **CEO url**, or simply select the **Paste CEO url from clipboard** button.
+ - Paste the URL from your CEO-SEPAL bridge project into the field marked **CEO URL**, or simply select the **Paste CEO URL from clipboard** button.
- Select :code:`Import CEO project`, which will populate the input file for the **Reference data** as well as the column names.
-- **Area data**: This is a .csv file that was automatically created during the Stratified Area Estimator – Design workflow. It contains area values for each mapped land cover class.
+- **Area data**: This is a .csv file that was automatically created during the SAE – Design workflow. It contains area values for each mapped land cover class.
- Select the **Area data** button.
- Open the folder starting with :code:`sae_design_`. As a reminder, if you exported your classification to the SEPAL workspace, the file will be in your SEPAL **Downloads** folder. Within this folder, select **area_rast.csv** (see image below).
@@ -36,21 +36,21 @@ Select the **Inputs** page on the left side of the screen. You will see two data
:width: 450
:align: center
-Next, you will need to adjust some parameters so that the tool recognizes the column names for your reference data and area data that contain the necessary information for your accuracy assessment. You should now see a populated **Required input** panel on the right side of the screen.
+Next, you will need to adjust some parameters so that the tool recognizes the column names for your reference data and area data that contain the necessary information for your accuracy assessment. You should now see a populated **Required input** pane on the right side of the screen.
Choose the column with the reference data information.
.. note::
- - For projects completed in CEO: This will either be your question name or the new column name you created in Part 2 above. Here, it is COLLECTED_CLASS following the directions.
- - For projects completed in CEO-SEPAL: ref_code
+ - **For projects completed in CEO**: This will either be your question name or the new column name you created in **Part 2** above. Here, it is **COLLECTED_CLASS**, following the directions.
+ - **For projects completed in CEO-SEPAL**: **ref_code**
Choose the column with the map data information.
.. note::
- - For projects completed in CEO: PL_MAP_CLASS
- - For projects completed in CEO-SEPAL: map_code
+ - **For projects completed in CEO**: **PL_MAP_CLASS**
+ - **For projects completed in CEO-SEPAL**: **map_code**
Select the map area column from the area file—map_area.
@@ -58,17 +58,17 @@ Choose the class column from the area **file—map_code** or **map_edited_class*
.. note::
- - For projects completed in CEO: Use **map_code** if you have a column in your reference data. If you use **map_edited_class** you must make sure that capitalization is correct.
- - For projects completed in CEO-SEPAL, use **map_code**.
+ - **For projects completed in CEO**: Use **map_code** if you have a column in your reference data. If you use **map_edited_class** you must make sure that capitalization is correct.
+ - **For projects completed in CEO-SEPAL**: Use **map_code**.
-You can add a **Display data** column to enable validation on-the-fly. You can choose any column from your CEO or CEO-SEPAL project. We recommend either your map class (e.g. PL_MAP_CLASS) or your reference data class (e.g. question name column). This example uses a CEO project.
+You can add a **Display data** column to enable validation on the fly. You can choose any column from your CEO or CEO-SEPAL project. We recommend either your map class (e.g. PL_MAP_CLASS) or your reference data class (e.g. question name column). This example uses a CEO project.
.. figure:: https://raw.githubusercontent.com/openforis/accuracy-assessment/master/aa_analysis/img/required_input_fields.png
:alt: The required input fields.
:width: 450
:align: center
-Once you have set these input parameters, select :code:`Check` on the left side of the window. This page will plot your samples on a world map. Fix the location of your plots by specifying the correct columns to use as the X and Y coordinates in the map. Select the dropdown menus and choose the appropriate coordinate columns for X and Y coordinates (the X coordinate should be LON; the Y coordinate should be LAT).
+Once you have set these input parameters, select :code:`Check` on the left side of the window. This page will plot your samples on a world map. Fix the location of your plots by specifying the correct columns to use as the X and Y coordinates in the map. Select the dropdown menus and choose the appropriate coordinate columns for X and Y coordinates (the X coordinate should be **LON**; the Y coordinate should be **LAT**).
Next, select the :code:`Results` page on the left side of the screen.
@@ -77,26 +77,26 @@ The **Results** page will display a few different accuracy statistics, including
The rows represent your assignments while the columns represent the map classifiers. The diagonal represents the number of samples that are in agreement, while the off-diagonal cells represent points that were not mapped correctly (or potentially not interpreted correctly).
.. figure:: https://raw.githubusercontent.com/openforis/accuracy-assessment/master/aa_analysis/img/confusion_matrix_output_sepal.png
- :alt: The Confusion matrix output by SEPAL.
+ :alt: The **Confusion matrix** output by SEPAL
:width: 450
:align: center
-Typically you would have to create the confusion table yourself and calculate the accuracies; however, the SAE-Analysis tool does this for you.
+Typically you would have to create the confusion table yourself and calculate the accuracies; however, the **SAE analysis** tool does this for you.
.. seealso::
- - If you completed previous sections, how does the SAE-Analysis tool's calculations compare with your own?
+ - If you completed previous sections, how does the **SAE analysis** tool's calculations compare with your own?
- You can download confusion matrices as tabular data (.csv) using the button.
Under **Area estimates**, the table shows you the area estimates, as well as producers' and users' accuracies, all of which were calculated from the error matrix and the class areas (sample weights) from the map product you are assessing.
-Estimations are broken up into simple and stratified estimates, each of which has its own confidence interval. In this exercise, we collected validation data using a stratified sample, so the values we need to use are the stratified random values. Note that all area estimates are in map units. You can change your desired **confidence interval** using the slider at the top of the panel. You can download area estimates as tabular data (.csv) using the button.
+Estimations are broken up into simple and stratified estimates, each of which has its own confidence interval. In this exercise, we collected validation data using a stratified sample, so the values we need to use are the stratified random values. Note that all area estimates are in map units. You can change your desired **Confidence interval** using the slider at the top of the panel. You can download area estimates as tabular data (.csv) using the button.
.. figure:: https://raw.githubusercontent.com/openforis/accuracy-assessment/master/aa_analysis/img/area_estimate.png
- :alt: The Area estimates screen in SEPAL.
+ :alt: The **Area estimates** screen in SEPAL
:align: center
-The **Graph** plots area estimates based on map pixel count, stratified random sample, simple random sample, unbiased stratified random, and direct estimate stratified random.
+The **Graph** plots area estimates based on map pixel count, stratified random sample, simple random sample, unbiased stratified random and direct estimate stratified random.
In this exercise, we collected validation data using a stratified sample, so the values we need to use are the stratified random values. We also need to define unbiased stratified random and direct estimate stratified random.
@@ -105,10 +105,11 @@ In this exercise, we collected validation data using a stratified sample, so the
Note that the **Map pixel count** value differs from these stratified random sample estimates. This shows how using a map pixel count is a poor estimation of actual area.
.. figure:: https://raw.githubusercontent.com/openforis/accuracy-assessment/master/aa_analysis/img/area_estimate_graph.png
- :alt: A graph of the area estimates based on different sample designs.
+ :alt: A graph of the area estimates based on different sample designs
:width: 450
:align: center
For support, `ask the community `__.
+
.. custom-edit:: https://raw.githubusercontent.com/openforis/accuracy-assessment/master/aa_analysis/doc.rst
diff --git a/docs/source/modules/dwn/sae_design.rst b/docs/source/modules/dwn/sae_design.rst
deleted file mode 100644
index a9bfcfbc08..0000000000
--- a/docs/source/modules/dwn/sae_design.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-sae_design
-==========
-
-.. warning::
-
- The english documentation of the module have is under construction.
-
-.. tip::
-
- For specific help, please open an issue on our repository by clicking on this `link `__.
-
-.. custom-edit:: https://github.com/openforis/sepal-doc/blob/master/docs/source/_templates/no_module.rst
diff --git a/docs/source/modules/dwn/sar_time_series.rst b/docs/source/modules/dwn/sar_time_series.rst
deleted file mode 100644
index cc36105397..0000000000
--- a/docs/source/modules/dwn/sar_time_series.rst
+++ /dev/null
@@ -1,12 +0,0 @@
-sar_time_series
-===============
-
-.. warning::
-
- The english documentation of the module have is under construction.
-
-.. tip::
-
- For specific help, please open an issue on our repository by clicking on this `link `__.
-
-.. custom-edit:: https://github.com/openforis/sepal-doc/blob/master/docs/source/_templates/no_module.rst
diff --git a/docs/source/modules/dwn/sdg_indicator.rst b/docs/source/modules/dwn/sdg_indicator.rst
index 230fa5410c..be655906d9 100644
--- a/docs/source/modules/dwn/sdg_indicator.rst
+++ b/docs/source/modules/dwn/sdg_indicator.rst
@@ -1,5 +1,7 @@
SDG Indicator 15.3.1
====================
+*Generate data for reporting on SDG Indicator 15.3.1*
+
The amount of degraded land relative to the total amount of land is measured by Sustainable Development Goal (SDG) Indicator 15.3.1.
@@ -9,7 +11,7 @@ To help achieve Target 15.3 of the SDGs, governments, international organization
This module provides guidance for generating data for reporting on SDG Indicator 15.3.1. It follows `Good practice guidance: SDG Indicator 15.3.1 – Proportion of land that is degraded over total land area (Version 2.0) `_.
-The methodology for the module (`Good practice guidance: SDG Indicator 15.3.1 – Proportion of land that is degraded over total land area [Version 1.0] `_) was implemented in consultation with the `Trends.Earth `_ team and `Conservation International `_.
+The methodology for the module, `Good practice guidance: SDG Indicator 15.3.1 – Proportion of land that is degraded over total land area (Version 1.0) `_, was implemented in consultation with the `Trends.Earth `_ team and `Conservation International `_.
Methodology
-----------
@@ -26,7 +28,7 @@ In 2017, the UNCCD released `the first version of their guidance `_ was published.
-The module is based on the most recent version of their guidance (Version 2), which outlines a comprehensive approach to land degradation and suggests methods for restoring degraded land by providing guidance for governments, businesses, local communities, and other stakeholders.
+The module is based on the most recent version of their guidance (Version 2), which outlines a comprehensive approach to land degradation and suggests methods for restoring degraded land by providing guidance for governments, businesses, local communities and other stakeholders.
Approach
""""""""
@@ -51,7 +53,7 @@ Three matrices are used to detect such changes in productivity:
**Productivity trend**
-The *productivity trend* measures the trajectory of change in productivity over time.
+**Productivity trend** measures the trajectory of change in productivity over time.
The `Mann–Kendall trend test `_ is used to describe the monotonic trend or trajectory (increasing or decreasing) of the productivity for a given time period.
@@ -65,7 +67,7 @@ To identify the scale and direction of the trend, a five-level scale is proposed
- Z score > 1.28 AND ≤ 1.96 = **Potentially improving**, or
-- Z score > 1.96 = Improving, as indicated by a significant increasing trend
+- Z score > 1.96 = **Improving**, as indicated by a significant increasing trend
The area of the lowest negative Z score level (< -1.96) is considered **degraded**, the area between Z score -1.96 to 1.96 is considered **stable**, and the area above 1.96 is considered **improved** for calculating the sub-indicator.
@@ -84,7 +86,7 @@ It is measured as follows:
\sigma = \sqrt{\frac{\sum_{n-15}^{n-3}(x_n-\mu)^2}{13}}
-where, :math:`x` is the productivity and :math:`n` is the year of analysis.
+where :math:`x` is the productivity and :math:`n` is the year of analysis.
The mean productivity of the current period is given as:
@@ -106,9 +108,9 @@ The area of the lowest negative Z score level (< -1.96) is considered **degraded
**Productivity performance**
-*Productivity performance* indicates the level of local land productivity relative to other regions with similar productivity potential.
+**Productivity performance** indicates the level of local land productivity relative to other regions with similar productivity potential.
-The maximum productivity index, :math:`NPP_{max}` value (90 :sup:`th` percentile) observed within the similar eco-region is compared to the observed productivity value (observed *NPP*). It is given as:
+The maximum productivity index, :math:`NPP_{max}` value (90:sup:`th` percentile) observed within the similar eco-region is compared to the observed productivity value (observed *NPP*). It is given as:
.. math:: \text{performance} = \frac{NPP_{observed}}{NPP_{max}}
@@ -167,7 +169,7 @@ Either of the following "look-up" tables can be used to calculate the sub-indica
Available Dataset:
-Sensors: MODIS; Landsat 4, 5, 7 and 8; Sentinel 2
+Sensors: MODIS; Landsat 4, 5, 7 and 8; Sentinel-2
NPP metric: NDVI; EVI and MSVI; Terra NPP
@@ -180,7 +182,7 @@ Default land cover dataset: ESA CCI land cover (1992–2020)
**Transition matrix for custom land cover legends**
-A custom transition matrix can be used in combination with the custom land cover legend. The matrix needs to be a comma-separated value (.csv) file in the following form:
+A custom transition matrix can be used in combination with the custom land cover legend. The matrix needs to be a .csv file in the following form:
The first two columns, excluding the first two cells (:math:`a_{31}...a_{n1} \text{and } a_{32}...a_{n2}`), must contain class labels and pixel values for the initial land cover, respectively.
@@ -270,7 +272,7 @@ Mandatory parameters
- **Vegetation index**: The vegetation index will be used to compute the trend trajectory (by default: **NDVI**).
-- **Trajectory**: There are three options available to calculate the productivity trend that describe the trajectory of change (by default, **productivity (VI) trend**).
+- **Trajectory**: There are three options available to calculate the productivity trend that describe the trajectory of change (by default, **Productivity (VI) trend**).
- **Land ecosystem functional unit**: Defaults to **Global Agro-Environmental Stratification (GAES)**; other available options include:
@@ -279,7 +281,7 @@ Mandatory parameters
- `Global Homogeneous Response Units `_; and
- Calculate based on the land cover (`ESA CCI `_) and soil texture (`ISRIC `_).
-- **climate regime**: Defaults to **Per pixel based on global climate data**; however, you can also use a fixed value everywhere using a predefined climate regime in the dropdown menu or select a custom value with the slider.
+- **Climate regime**: Defaults to **Per pixel based on global climate data**; however, you can also use a fixed value everywhere using a predefined climate regime in the dropdown menu or select a custom value with the slider.
.. _sdg-advanced-parameters:
diff --git a/docs/source/modules/dwn/sepafe.rst b/docs/source/modules/dwn/sepafe.rst
index 0ee0384cb5..593a2054ca 100644
--- a/docs/source/modules/dwn/sepafe.rst
+++ b/docs/source/modules/dwn/sepafe.rst
@@ -1,33 +1,34 @@
SEPAFE
======
+*Receive and validate fire alerts from the FIRMS programme by using daily Planet imagery*
-The SEPAL Planet Active Fires Explorer (SEPAFE) is a module developed on the SEPAL platform based on the `Fire Information for Resource Management System (FIRMS) `_ and using Planet Scope imagery from Planet Labs.
+The **SEPAL Planet Active Fires Explorer (SEPAFE)** is a module developed on the SEPAL platform based on the `Fire Information for Resource Management System (FIRMS) `_ and using Planet Scope imagery from Planet Labs.
-SEPAFE aims to provide users with a quick way to receive and validate fire alerts from the FIRMS programme by using daily Planet imagery.
+**SEPAFE** aims to provide users with a quick way to receive and validate fire alerts from the FIRMS programme by using daily Planet imagery.
.. image:: https://raw.githubusercontent.com/dfguerrerom/planet_active_fires_explorer/main/doc/img/sepal_active_fires_home.png
-Settings panel
---------------
+Settings pane
+-------------
-The **Settings** panel is composed of three tabs: :code:`Planet Imagery`, :code:`Area of Interest` and :code:`Alerts` (all necessary to receive fire alerts).
+The **Settings** pane is composed of three tabs: :code:`Planet Imagery`, :code:`Area of Interest` and :code:`Alerts` (all necessary to receive fire alerts).
-.. tip:: The **Settings** panel can be open and closed by selecting the settings button (:btn:``).
+.. tip:: The **Settings** pane can be open and closed by selecting the **Settings** button (:btn:``).
-Connect your Planet API Key
+Connect your Planet API key
^^^^^^^^^^^^^^^^^^^^^^^^^^^
-.. note:: This step is optional but highly recommended. While you can receive fire alerts from different satellite sources by simply going through the next tabs, a Planet API Key with access to daily imagery will allow you to fully leverage the module.
+.. note:: This step is optional but highly recommended. While you can receive fire alerts from different satellite sources by simply going through the next tabs, a Planet API key with access to daily imagery will allow you to fully leverage the module.
-- Validate your Planet API Key: Provide a valid API Key in the input box and select the validation button. The module will check whether the key is valid and messages related to its connection will be displayed within the alerts widget. Once your validation is done, you can open the **Advanced settings** expansion panel and modify its inputs.
+- Validate your Planet API key: Provide a valid API key in the input box and select the **Validation** button. The module will check whether the key is valid and messages related to its connection will be displayed within the **Alerts** widget. Once your validation is done, you can open the **Advanced settings** expansion panel and modify its inputs.
.. tip:: Use the **Planet state** bar located in the upper-left corner to receive informative messages about Planet imagery (e.g. problems with the key, number of images loaded).
-- Open the **Advanced settings** panel:
+- Open the **Advanced settings** pane:
- :code:`Max number of images`: Maximum number of planet images to be displayed at once in each alert.
- - :code:`Search up to 'x' days before`: Number of days before the fire alert date to look up for the best image available.
- - :code:`Search up to 'x' days after`: Number of days after the fire alert date to look up for the best image available.
+ - :code:`Search up to 'x' days before`: Number of days before the fire alert date to search for the best image available.
+ - :code:`Search up to 'x' days after`: Number of days after the fire alert date to search for the best image available.
- :code:`Cloud cover threshold`: Maximum cloud cover threshold accepted in the images (based on Planet metadata).
.. image:: https://raw.githubusercontent.com/dfguerrerom/planet_active_fires_explorer/main/doc/gif/planet.gif
@@ -38,26 +39,29 @@ Select area of interest (AOI)
The module has two options for selecting an AOI to filter alerts.
-- Draw a shape: When selected, three drawing tools will be displayed in the upper-left corner of the map; you can select a `square`, `circle`, or a `polygon`, and draw them on the map.
-- Select a country: Enter the name of the country into the search box (or navigate through it by using the scroll bar) and select the desired country. Once the country is selected, it will be displayed in the map view.
+- **Draw a shape**: When selected, three drawing tools will be displayed in the upper-left corner of the map; you can select a `square`, `circle`, or a `polygon`, and draw them on the map.
+- **Select a country**: Enter the name of the country into the search box (or navigate through it by using the scroll bar) and select the desired country. Once the country is selected, it will be displayed in the map view.
.. image:: https://raw.githubusercontent.com/dfguerrerom/planet_active_fires_explorer/main/doc/gif/aoi.gif
Receive fire alerts
^^^^^^^^^^^^^^^^^^^
-- Recent: Products to be downloaded are coming from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) for the last 24 hours, 28 hours, and the last 7 days.
+- **Recent**: Products to be downloaded are coming from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Visible Infrared Imaging Radiometer Suite (VIIRS) for the last 24 hours, 28 hours, and the last 7 days.
-- Historical: For historical products, you will be able to download alerts from 2000 until the last finished year from the MODIS satellite. Select the historical button and filter by the dates.
+- **Historical**: For historical products, you will be able to download alerts from 2000 until the last finished year from the MODIS satellite. Select the **Historical** button and filter by the dates.
-After selecting the `get alerts` button, the module will start the download process and clip the alerts to the given AOI. The alerts will be displayed on the map according to a color map for alert confidence – ranging from green to orange to red for the confidence values high, nominal, and low (for VIIRS), and >80, >70 < 80, <50 (for MODIS).
+After selecting the **Get alerts** button, the module will start the download process and clip the alerts to the given AOI. The alerts will be displayed on the map according to a color map for alert confidence – ranging from green to orange to red for the following confidence values:
-.. attention:: Depending on the sensor source and whether your alert request is recent or historical, the download/clip process could take more time. This module is intended to get a quick validation of fire alerts. If you are requesting an area with more than 10000 fire alerts, you will be asked if you want to display all the alerts on the map — which could significantly effect the performance of the tool — or if you want to download them to your desktop/SEPAL environment.
+- **high, nominal, and low** (for VIIRS)
+- **>80, >70 < 80, <50** (for MODIS)
+
+.. attention:: Depending on the sensor source and whether your alert request is recent or historical, the download/clip process could take more time. This module is intended to get a quick validation of fire alerts. If you are requesting an area with more than 10000 fire alerts, you will be asked if you want to display all the alerts on the map — which could significantly affect the performance of the tool — or if you want to download them to your desktop/SEPAL environment.
Navigate through alerts
-----------------------
-Once the alerts are displayed on the map, you will be able to navigate through each of them by using the :code:`navigation widget`. Use the :code:`next` and :code:`prev` buttons to navigate through the fire alerts; use the :code:`confidence` dropdown list to filter the alerts by the confidence (see `What is the detection confidence? `_).
+Once the alerts are displayed on the map, you will be able to navigate through each of them by using the :code:`navigation widget`. Use the :code:`next` and :code:`prev` buttons to navigate through the fire alerts; use the :code:`confidence` dropdown list to filter the alerts by confidence (see `What is the detection confidence? `_).
The confidence value was added to help users gauge the quality of individual fire pixels included in the Level 2 fire product. The confidence field should be used with caution; in different parts of the world, it will likely vary in meaning. Nevertheless, some of our end users have found such a field to be useful in excluding false-positive occurrences of fire. They are different for MODIS and VIIRS.
@@ -65,11 +69,14 @@ The confidence value was added to help users gauge the quality of individual fir
.. tip:: You can activate and deactivate the fire navigation widget by selecting the **Fires** button (:btn:``).
-.. tip:: Planet parameters can be changed at any time. To refresh results from the current alert, select the refresh button (:btn:``).
+.. tip:: Planet parameters can be changed at any time. To refresh results from the current alert, select the **Refresh** button (:btn:``).
Manually load planet imagery
----------------------------
-Select any point on the map and use the refresh icon (:btn:``) to retrieve Planet imagery using the parameters set in Step 1; the module will use the current acquisition alert date to search the images. This option is useful when you want to explore surrounding areas close to the alert point, but without alerts to display.
+Select any point on the map and use the **Refresh** icon (:btn:``) to retrieve Planet imagery using the parameters set in **Step 1**; the module will use the current acquisition alert date to search the images. This option is useful when you want to explore surrounding areas close to the alert point, but without alerts to display.
.. attention:: This option requires a valid Planet Level 2 key; otherwise, you will receive an error message in the **Status** bar.
+
+
+.. custom-edit:: https://raw.githubusercontent.com/sepal-contrib/planet_active_fires_explorer/release/doc/en.rst
diff --git a/docs/source/modules/dwn/sepal_mgci.rst b/docs/source/modules/dwn/sepal_mgci.rst
index 33f2eb3f0f..6754c337b1 100644
--- a/docs/source/modules/dwn/sepal_mgci.rst
+++ b/docs/source/modules/dwn/sepal_mgci.rst
@@ -1,380 +1,614 @@
-SEPAL - Mountain Green Cover Index (MGCI) :sub:`beta`
-=====================================================
+SEPAL-SDG 15.4.2 :sub:`beta`
+============================
-*A tool to support the computation of Sustainable Development Goal (SDG) Indicator 15.4.2 – Mountain Green Cover Index*
+*A tool to support the computation of SDG Indicator 15.4.2 Indicator 15.4.2: (a) Mountain Green Cover Index and (b) Proportion of degraded mountain land using SEPAL (System for Earth Observation Data Access, Processing, and Analysis for Land Monitoring).*
-General information
--------------------
+This guide will introduce you to SEPAL-SDG 15.4.2 :sub:`beta` and will provide you detailed instructions on how the set it up and carry out the computation of both sub-indicators in a step-by-step manner. Screenshots are included to make it easier for the user to know what each description refers to.
-About SEPAL–MGCI :sub:`beta`
-^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-**SEPAL-MGCI :sub:`beta`** was developed to help member countries compute and report against SDG Indicator 15.4.2, as well as track the progress of efforts to monitor ecosystem restoration.
+General Information
+-------------------
-It is available on the SEPAL platform in a beta stage (i.e. still in development).
+About SEPAL-SDG 15.4.2 :sub:`beta`
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-If you have questions or concerns, use the `SEPAL-MGCI GitHub issue tracker `__, follow our documentation's `Contribution guidelines `__.
+SEPAL-SDG 15.4.2 :sub:`beta` has been developed by the Food and Agriculture Organization (FAO) of the United Nations to support national authorities to compute and report against SDG Indicator 15.4.2. SEPAL-SDG 15.4.2 :sub:`beta` is built on SEPAL, an open-source cloud-based platform that allows users to query and process satellite data quickly and efficiently, tailor products for local needs, and produce sophisticated and relevant geospatial analyses quickly. To know more about SEPAL visit the `SEPAL website `_.
-Please contact the **SEPAL-MGCI :sub:`beta` team** with any comments or suggestions.
+SEPAL-SDG 15.4.2 :sub:`beta` is in a beta stage and therefore it is still under development. If you have specific bugs to report or improvements to the tool that you would like to suggest, please use the `GitHub’s issue tracker `_ of SEPAL-SDG 15.4.2 :sub:`beta` and do follow the `contribution guidelines `_.
-Authors
+Authors
^^^^^^^
-**SEPAL-MGCI :sub:`beta`** was developed by the Food and Agriculture Organization of the United Nations (FAO).
-
-Specific contributors to **SEPAL-MGCI :sub:`beta`** and its documentation include:
+SEPAL-SDG 15.4.2 :sub:`beta` has been developed by the Food and Agriculture Organization (FAO) of the United Nations, and has been funded by Norway's International Climate and Forest Iniciative (NICFI).
-- Daniel Guerrero
-- Pierrick Rambaud
-- Corinna Ravilious
-- Xavier de Lamo
+SEPAL-SDG 15.4.2 :sub:`beta` has been developed by Daniel Guerrero. Ann Cheptoo Rotich developed the technical documentation. Xavier de Lamo led and supervised the development of both products.
License
^^^^^^^
-**SEPAL-MGCI :sub:`beta`** is free and open-source. It is licensed under an `MIT license `__.
-
-The documentation is made available under the terms of the `Creative Commons Attribution 4.0 International License (CC BY 4.0) `__. The boundaries, names and designations used do not imply official endorsement or acceptance by the United Nations.
+SEPAL-SDG 15.4.2 :sub:`beta` is free and open source. It is licensed under `MIT license `_. The documentation is made available under the terms of the `Creative Commons Attribution 4.0 International License (CC BY 4.0) `_.
Data sources
^^^^^^^^^^^^
-**SEPAL-MGCI** draws on a number of global data sources to allow the computation of SDG Indicator 15.4.2 when national data is not available.
+SEPAL-SDG 15.4.2 :sub:`beta` draws on a number of global data sources to allow the computation of the SDG 15.4.2 indicator when similar national data is not available. The datasets described below have been made available by the following organizations under separate terms as indicated in their respective metadata.
-The datasets described below have been made available by the following organizations under separate terms, as indicated in their respective metadata:
+- **Land cover**: European Space Agency (ESA) Climate Change Initiative (CCI) Land cover, available at `ESA-CCI land cover website `_.
+- **Digital Elevation Model**: The Shuttle Radar Topography Mission (SRTM), available at `Google Earth Engine Data SRTM `_.
+- **Administrative Boundaries**: FAO GAUL: Global Administrative Unit Layers 2015, available at `Google Earth Engine Data FAO GAUL `_.
-- **Land cover**: European Space Agency (ESA) Climate Change Initiative (CCI) land cover available at the `ESA-CCI viewer `__.
+**Note:** The Administrative Boundaries provided in this tool are in the public domain. The designations employed and the presentation of material on this map do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or its authorities, or concerning the delimitation of its frontiers or boundaries. If using SEPAL-SDG 15.4.2 :sub:`beta` for official purposes, it is recommended that users use an official boundary provided by the designated office of their country.
-- **Digital elevation model**: The Shuttle Radar Topography Mission (SRTM), available via `Google Earth Engine (GEE) `__.
+Before using SEPAL-SDG 15.4.2 :sub:`beta`
+-----------------------------------------
-- **Administrative boundaries**: FAO Global Administrative Unit Layers (GAUL) 2015, available via `GEE `__.
+Initial setup
+^^^^^^^^^^^^^
+SEPAL is closely linked to Google Earth Engine (GEE), a Google-powered Earth-observation cloud-computing platform, as it builds in many of its functionalities. This means that to run SEPAL-SDG 15.4.2 :sub:`beta` you will need to have **connected SEPAL and GEE accounts**.
-.. note:: The **Administrative boundaries** provided in **SEPAL-MGCI** are in the public domain. Designations employed and presentation of material on this map do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city, or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. If using **SEPAL-MGCI** for official purposes, it is recommended that users use an official boundary provided by the designated office of their country.
+- To **Create a SEPAL account** please follow the `registration steps described here `_ and then familiarize yourself with the tool by exploring its interface.
+- To **Create a Google Earth Engine (GEE) account** please follow these `steps `_ and don't forget to `initialize the home folder `_.
+- To **Connect your SEPAL and GEE accounts** follow the `instructions described here `_.
-Background
-^^^^^^^^^^
+Data Requirements
+^^^^^^^^^^^^^^^^^
+SDG Indicator 15.4.2 requires several spatial data inputs to be computed. These include:
-SDG Indicator 15.4.2 – Mountain Green Cover Index (MGCI) is one of the two indicators under SDG Target 15.4, which aims to "ensure the conservation of mountain ecosystems, including their biodiversity, to enhance their capacity to provide benefits which are essential for sustainable development".
+- **A Mountain Area Map:** For the purposes of standardization and international comparability of indicators values, SDG Indicator 15.4.2 adheres to the UNEP-WCMC mountain definition (UNEP-WCMC, 2002). The UNEP-WCMC method defines total global mountain area as the sum of seven classes (commonly known as ‘Kapos mountain classes’), based on elevation, slope and local elevation ranges parameters. This mountain area is subdivided into bioclimatic belts (Nival, Alpine, Montane, and Remaining Mountain Area) based on average temperatures as defined by Körner et al. (2011). A global mountain area map based on these definitions and methodologies has been developed by FAO and is used by default by SEPAL-SDG 15.4.2 :sub:`beta` as part of the computations.
-FAO is the custodian agency of this indicator. The MGCI is designed to measure the extent and changes of green vegetation in mountain areas to monitor progress towards SDG Target 15.4.
+- **A National Administrative Boundary for the country of interest:** SEPAL-SDG 15.4.2 :sub:`beta` has been conceived to facilitate the computation of of SDG Indicator 15.4.2 at country level. To facilitate access to this , SEPAL-SDG 15.4.2 :sub:`beta` uses as a default global data source for national boundaries: the FAO GAUL Global Administrative Unit Layers 2015 data set. However, the tool also allows national agencies to use their own national country boundary layer if available.
-The MGCI is defined as the ratio of the mountain green cover area to the total mountain area:
+- **A collection of Land Cover Maps for the country of interest:** Land cover maps represent spatial information on different types (classes) of physical coverage of the Earth's surface, e.g. forests, grasslands, croplands, lakes, wetlands. This data serves different functions for SDG Indicator 15.4.2: In Sub-Indicator 15.4.2a, land cover is used to categorize land into green and non-green cover areas. In Sub-Indicator 15.4.2b, land cover is used to identify areas where changes in the type of land cover may indicate a decline or loss of biodiversity, mountain ecosystem functions or services that are considered desirable in a local or national context. The collection of land cover maps to compute this indicator should be available from the year 2000. Simlarly, to the national administrative boundary dataset, SEPAL-SDG 15.4.2 :sub:`beta` provides access to the default land cover maps selected by FAO to compute the indicator (see Data Sources section). However, it also facilitates national authorities to use relevant national or regional land cover datasets. These datasets should also be available as GEE assets as an `image collection `_ in order to allow SEPAL-SDG 15.4.2 :sub:`beta` to access it. The next section of the tutorial will explain you how to do this.
-.. math::
-
- MGCI = (Mountain Green Cover Area)/(Total Mountain Area)
+Uploading files into Google Earth Engine
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+GEE allows the uploading of different types of data formats: shapefiles, raster images, image collection and CSV tables. This section will show how to upload different data types into the platform.
-Where:
+1. Go to **Assets** in the top left panel in the **Earth Engine Code Editor** page. Clicking on it will open the **Asset Manager**
-- **Mountain green cover area**: Sum of mountain area (km :sup:`2`) covered by cropland, grassland, forestland, shrubland and wetland, as defined based on the Intergovernmental Panel on Climate Change (IPCC) classification (Penman *et al.*, 2003). This component is calculated from the vegetation descriptor layer.
-- **Total mountain area**: Total area (km :sup:`2`) of mountains. In both the numerator and denominator, mountain area is defined according to Kapos *et al.* (2000). This component is calculated from the mountain descriptior layer.
-- **Vegetation descriptor layer**: The vegetation descriptor layer categorizes land cover into green and non-green areas. Green vegetation includes both natural vegetation and vegetation resulting from anthropic activity (e.g. crops, afforestation). Non-green areas include very sparsely vegetated areas, bare land, water, permanent ice/snow, and urban areas. The vegetation descriptor layer is derived from a land cover map, where land cover categories are classified into IPCC categories and then in green/non-green areas.
-
- .. _ipcc_classes:
-
- .. csv-table:: IPCC classification
- :header: "Code", "Description"
- :widths: auto
- :align: center
-
- "1","Forest"
- "2","Grassland"
- "3","Cropland"
- "4","Wetland"
- "5","Settlement"
- "6","Other land"
-
-
-- **Mountain descriptor layer**: The mountain descriptor layer consists of a map of mountain classes following the UN Environment Programme World Conservation Monitoring Centre (UNEP-WCMC) classification (Kapos *et al.*, 2000), which classifies world mountain areas according to altitude, slope and elevation range into the following categories:
-
- .. csv-table:: Mountain classes
- :header: "UNEP-WCMC Mountain Class", "Description"
- :widths: auto
- :align: center
-
- "1","Elevation > 4.500 m"
- "2","Elevation 3.500–4.500 m"
- "3","Elevation 2.500–3.500 m"
- "4","Elevation 1.500–2.500 m and slope > 2"
- "5","Elevation 1.000–1.500 m and slope > 5 or local elevation range (LER 7 km radius) > 300 m"
- "6","Elevation 300–1.000 m and local elevation range (7 km radius) > 300 m"
-
-**SEPAL-MGCI :sub:`beta`** allows the user to compute each of these descriptive layers to then calculate MGCI values for any given area using both global and user-provided data. The results of this analysis can then be exported to a set of standardized reporting tables where MGCI values are disaggregated by mountain class and IPCC land category, as specified in the `metadata of SDG Indicator 15.4.2 `_.
-
-References
-^^^^^^^^^^
-
-- Kapos, V., Rhind, J., Edwards, M., Prince, M. & Ravillous, C. 2000. Developing a map of the world’s mountain forests. In: *Forests in Sustainable Mountain Development: A State-of-Knowledge Report for 2000*. pp. 4–9. Wallingford, CAB International.
-- Penman, J., Gytarsky, M., Hiraishi, T., Krug, T., Kruger, D., Pipatti, R., Buendia, L., Miwa, K., Ngara, T. & Tanabe, K. 2003. *Good Practice Guidance for Land Use, Land-use Change and Forestry*.
-
-Before using SEPAL-MGCI :sub:`beta`
------------------------------------
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/setting_up/uploading_gee/new%20button.PNG
+ :align: center
+ :width: 900
+ :alt: GEE_Interface
-To run the **SEPAL-MGCI** module you will need:
+2. Select **New**. You will have several choices, including **Raster** (Geotiffs or TFRecords), **Vector* (Shapefiles) and **Data tables** (csv files), which will be described in the following subsections.
-- a web browser
-- an internet connection
-- SEPAL and Google Earth Engine (GEE) accounts
+2.3.1 Uploading a vector file
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+1. In SEPAL-SDG 15.4.2 :sub:`beta` custom country boundaries need to be uploaded in vector format. To do so, choose **Shapefiles**. A pop-up window will appear. Navigate to the location of your data.
+2. In the pop-up window, select the file you want to upload from your computer. You can upload the vector data in a compressed mode as a :code:`.zip` file. If not, remember that the a :code:`.shp` file alone is not sufficient and must be accompanied with other files describing the vector data.
- - **SEPAL**: The environment where the SEPAL-MGCI :sub:`beta` is deployed and therefore displayed. To create a SEPAL account, please follow the `registration steps `__. Then, familiarize yourself with the tool by exploring its interface.
- - **Google Earth Engine (GEE)**: SEPAL-MGCI :sub:`beta` has been built under the GEE Python API, which means that all computational steps are done through GEE servers. To open a GEE account, follow the `registration steps `__, remembering to `initialize the home folder `__.
- - **Connect your SEPAL and GEE accounts**: The last step is to connect both accounts by following `these step-by-step instructions `__.
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/setting_up/uploading_gee/all%20files%20listed.PNG
+ :align: center
+ :width: 300
+ :alt: Vector_File
+
+Any file errors will be highlighted by the uploader, as in the example below:
-SEPAL interface
----------------
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/setting_up/uploading_gee/error%20message.PNG
+ :align: center
+ :width: 500
+ :alt: Vector_Error
-If you are new to SEPAL, it is recommended to take a review the interface and familiarize yourself with the main tools. A detailed description of the features can be consulted in the `interface documentation `__.
+3. Once all files are loaded correctly, they are displayed in the task manager. Typically this process takes a couple of minutes depending on the size of the dataset. The progress of the upload is displayed in the task manager as shown below:
-To open SEPAL-MGCI :sub:`beta`:
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/setting_up/uploading_gee/task%20manager.PNG
+ :align: center
+ :width: 300
+ :alt: vector_uploading_process
-- use the `apps tab `__ and navigate through the pages, or
-- enter "Mountain Green Cover Index" into the search box, select the app drawer, and wait until the SEPAL-MGCI :sub:`beta` module has been displayed in your session (it may take a few minutes). The module should look like the following image:
+4. The uploaded assets will be listed in the Assets List under the Assets tab. If not displayed, click on the Refresh button.
-.. image:: https://raw.githubusercontent.com/dfguerrerom/sepal_mgci/master/doc/img/0_app_overview.PNG
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/setting_up/uploading_gee/upload_success.PNG
:align: center
- :width: 600
- :alt: MGCI module
+ :width: 700
+ :alt: Assets_listed
+
+5. Clicking on the asset will open a pop-window. The asset is ready for use. You can now visualize, share or delete it accordingly it entirely:
+
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/setting_up/uploading_gee/asset%20details.PNG
+ :align: center
+ :width: 800
+ :alt: asset_popupwindow
-SEPAL-MGCI :sub:`beta` module
------------------------------
+Uploading a raster file
+^^^^^^^^^^^^^^^^^^^^^^^
-SEPAL-MGCI :sub:`beta`, as any other SEPAL module, is divided into two main sections:
+1. In SEPAL-SDG 15.4.2 :sub:`beta`, land cover maps need to be uploaded as raster files and made available as an `image collection `_. To do so, select **Image**.
-- **Process drawers**: Find processing steps to accomplish the goal of the module, which consists of four steps:
+2. In the pop-up window, select the file you want to upload from your computer (compatible formats include :code:`.tiff`, :code:`.tif`, :code:`.json`, :code:`.tfrecord` or :code:`.tfrecord.gz`; the name of your asset can be changed in the next text field). By default, the asset will be named after the basename. Please ensure that the name includes the reference year of the land cover map.
- - Area of interest (AOI) selection
- - Mountain descriptor
- - Vegetation descriptor
- - MGCI results
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/setting_up/uploading_gee/geotiff_upload.PNG
+ :align: center
+ :width: 300
+ :alt: Geotiff_upload
-- **Help drawers**: Learn more about the tool, objectives, and background on the module's development. This consists of:
+3. Repeat step 2 for each of the land cover maps.
- - Source code: The module was developed under an `MIT license `__, meaning the development is freely accessible and the code is public (it will link you to the GitHub repository of the module).
- - Wiki: The latest documentation on SEPAL-MGCI :sub:`beta`, where you can start learning the workflow and module features.
- - Bug report: Use this section to report any unexpected results or behaviours by following the `contribution guidelines `__.
+4. Once all the land cover maps have been uploaded, you can create an image collection following `Google Earth Engine good practice guidelines on the topic `_.
-Area of interest (AOI)
-----------------------
+Uploading a table file
+^^^^^^^^^^^^^^^^^^^^^^
+Google Earth Engine allows the upload of tabular data in CSV format. To upload a table file do the following:
-The calculation of the MGCI will be restricted to a specific AOI. In this step, you will have the option to choose between a predefined list of administrative layers or use a custom dataset. The available options include:
-
-- Predefined layers:
- - Country/province
- - Administrative level 1
- - Administrative level 2
-
-- Custom layers
- - Vector file: Use this option to upload a custom vector file. Select the **Vector file** method in the dropdown list; a **File manager** will be displayed, allowing you to search and select a vector file stored in your **SEPAL environment** (see `How to exchange files with SEPAL `__). The dropdown menu **Column** is useful to filter the features of the vector file. The default option is **Use all features**. To filter the collection, select a **Column** and a **Value** in the corresponding dropdown list, then select the :guilabel:`Select aoi` button.
-
- .. image:: https://raw.githubusercontent.com/dfguerrerom/sepal_mgci/master/doc/img/1_vector_file.PNG
- :align: center
- :width: 600
- :alt: AOI selection
-
- .. note:: The AOI tool will read the following formats: [".shp", ".geojson", ".gpkg", ".kml"]; it will transform its original coordinates into EPSG:4326.
-
- - GEE asset name: See how to `upload an asset in GEE `__.
+1. Select New > **csv file upload**.
+2. In the pop-up window that appears, select the file you want to upload from your computer (note: compatible formats include :code:`.csv`, :code:`.json`).
-Since all processing is done in GEE, custom layers have to be previously stored as an `Earth Engine asset `__ in your GEE account (either private or in a third-party account as a public asset; see `How to upload an asset to GEE `__). The dropdown menu will query all assets in your GEE folder that matches the image type. Select it from the dropdown menu or enter it directly.
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/setting_up/uploading_gee/uploading_csv.PNG
+ :align: center
+ :width: 300
+ :alt: Geotiff_upload
-.. attention::
+.. tip::
- The administrative boundaries provided in SEPAL-MGCI are in the public domain. The designations employed and the presentation of material on this map do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city, or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. If using SEPAL-MGCI for official purposes, it is recommended that users use an official boundary provided by the designated office of their country.
+ Now you can access and use your assets in SEPAL. As you have already established a connection between your GEE and SEPAL accounts, all your assets are synced and available for you in SEPAL. You will be able to select them from the dropdown or copy/paste them directly from GEE when prompted in SEPAL-SDG 15.4.2 :sub:`beta`
-After selecting the desired area, select the :guilabel:`Select AOI` button; the map will display your selection.
+The SEPAL interface and the SEPAL-SDG 15.4.2 :sub:`beta` module
+---------------------------------------------------------------
-.. note::
+If you are new to SEPAL, it is recommended to take a look over the interface and familiarize yourself with the main tools. A detailed description of the features can be consulted in the `interface documentation `_.
- You can only select one AOI. In some cases, depending on the input data, the process could take longer (see the :ref:`calculation ` section for more info).
-.. image:: https://raw.githubusercontent.com/dfguerrerom/sepal_mgci/master/doc/img/1_aoi_selection.PNG
+Setting up a SEPAL instance
+^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Applications such as the SEPAL-SDG 15.4.2 :sub:`beta` make use of SEPAL instances; running them will use your SEPAL computing resources. Selecting an app automatically initiates the process and starts the smallest instance to run the SEPAL sandbox. However, in some cases, especially where more powerful processing is required, you might need larger instances. For this reason, in some cases you may need manually set up a larger SEPAL instance before running SEPAL-SDG 15.4.2 :sub:`beta`. To do that do the following:
+
+1. Go to the `SEPAL terminal `_ (blue icon in the left panel in the image below) and wait for the instance selector to start.
+
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/defining_e/sepal_terminal.PNG
:align: center
- :width: 600
- :alt: AOI selection
+ :width: 300
+ :alt: Geotiff_upload
-Mountain descriptor layer
--------------------------
+2. Type the instance name. In our case m2 or m4 should suffice, then press ENTER.
+3. Wait for the instance to finish loading.
+4. Once completed, go back to the dashboard of the application to launch your app. It will automatically use the instance you have set.
-This section of SEPAL-MGCI :sub:`beta` produces a UNEP-WCMC mountain class map for the study area selected in the previous step using a **Digital elevation model (DEM)** as an input. You have the option to provide a custom DEM for your study area or use the Shuttle Radar Topography Mission (SRTM) DEM (90 m resolution) developed by NASA/CGIAR.
+Opening SEPAL-SDG 15.4.2 :sub:`beta`
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-Questionnaire
-^^^^^^^^^^^^^
+To open the the SEPAL-SDG 15.4.2 :sub:`beta` module use the `apps tab `_ and navigate through the list of apps until you find the module (alternatively, you can type in the search box "SDG 15.4.2"). Once you have find it, click over the app drawer and wait patiently until SEPAL-SDG 15.4.2 :sub:`beta` module is displayed (it may take a few minutes).
-Here you have to indicate the DEM dataset you wish to use to develop the mountain class map. If you wish to use your own DEM dataset, select **Yes**. By selecting the desired option, the module will hide or display a text box to insert or select an asset ID.
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/defining_e/sepal_app.PNG
+ :align: center
+ :width: 700
+ :alt: MGCI module
+
+The module should look like the image below. As any other SEPAL module, SEPAL-SDG 15.4.2 :sub:`beta` is divided into two main sections:
-.. image:: https://raw.githubusercontent.com/dfguerrerom/sepal_mgci/master/doc/img/2_questionaire.PNG
+- **Process drawers**: Located on the top left of the interface. This is where you can find the processing steps to accomplish the goal of the module. In SEPAL-SDG 15.4.2 beta, this is composed by 4 processing steps: Area of interest; Land cover settings; Indicator settings and Results.
+
+- **Help drawers**: Located just below the process drawers. This is used to describe the tool, the objectives and give a background about how it was developed. This is composed by the source code (the module was developed under a MIT license, which means that the development is freely accessible, and the code is public in GitHub); the Wiki (the latest documentation on tool) and the Bug report (use this section to report any unexpected result or behavior. To do so, please follow the `contribution guidelines `_.)
+
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/App_landing.PNG
:align: center
- :width: 300
- :alt: DEM questionnaire
+ :width: 700
+ :alt: MGCI module
-Custom dataset
-::::::::::::::
+Personalising SEPAL-SDG 15.4.2 :sub:`beta`
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-Since all processing is done in GEE, all inputs must be uploaded as an `Earth Engine asset `__. When using a custom dataset, it must be stored in your GEE account (private or in a third-party account as a public asset). The dropdown menu will query all assets in your GEE folder that match the image type. You can select it from the dropdown menu or enter it directly.
+SEPAL includes functionalities to personalize the appearance of SEPAL-SDG 15.4.2 :sub:`beta`
-After selecting the :guilabel:`Create UNEP-WCMC Mountain Class Map` button, the module will create the mountain descriptor layer; it will be automatically displayed on the map.
+**Theme customization:**
+You can choose between a dark or light theme. To change the theme, click the light mode icon (highlighted) at the top ribbon of the interface. The application will need to be restarted to apply the changes.
-.. image:: https://raw.githubusercontent.com/dfguerrerom/sepal_mgci/master/doc/img/2_mountain_descriptor.PNG
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/Theme_light.PNG
:align: center
- :width: 600
- :alt: Mountain layer example
+ :width: 700
+ :alt: MGCI module
-Vegetation descriptor layer
----------------------------
+**Language selection:**
+SEPAL-SDG 15.4.2 :sub:`beta` is currently only available in English. New language versions will be made available soon.
-This section of SEPAL-MGCI :sub:`beta` produces the vegetation descriptor layer needed to compute the MGCI for the selected study area. It does so by reclassifying a land cover map into the six :ref:`IPCC land cover classes ` (Forest, Cropland, Grassland, Wetland, Settlements and Other Land), and then into green and non-green cover following the reclassification rules specified in the indicator’s metadata.
+Calculating SDG Indicator 15.4.2
+--------------------------------
-Questionnaire
-^^^^^^^^^^^^^
+Conceptual framework
+^^^^^^^^^^^^^^^^^^^^
+This section will guide you through the sequence of processing steps to calculate SDG indicator 15.4.2. The main goal is to assess the changes in land cover in mountain areas by bioclimatic belts. The algorithm works using land cover data, a digital elevation model, a mountain area map and a national administrative boundary layer.
+
+Overview of Sub-Indicator 15.4.2a (Mountain Green Cover Index)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-Here you have to indicate the land cover map that you wish to use to compute the vegetation descriptor layer. If you wish to use your own land cover map, select :guilabel:`yes`. If you select :guilabel:`no`, SEPAL-MGCI :sub:`beta` will use the CCI land cover datasets developed by the European Space Agency (ESA) for the years 1992–2018 (at 300 m resolution) to produce the vegetation descriptor layer for the selected AOI.
+**Sub-indicator 15.4.2a**, Mountain Green Cover Index (MGCI), is designed to measure the extent and changes of green cover - i.e. forest, shrubs, trees, pasture land, cropland, etc. – in mountain areas. MGCI is defined as the percentage of green cover over the total surface of the mountain area of a given country and for given reporting year. The aim of the index is to monitor the evolution of the green cover and thus assess the status of conservation of mountain ecosystems.
-.. image:: https://raw.githubusercontent.com/dfguerrerom/sepal_mgci/master/doc/img/3_questionnaire.PNG
+.. math::
+
+ MGCI = (Mountain Green Cover Area n)/(Total Mountain Area)
+
+Where:
+
+- **Mountain Green Cover Area n** = Sum of areas (in km2) covered by (1) tree-covered areas, (2) croplands,(3) grasslands, (4) shrub-covered areas and (5) shrubs and/or herbaceous vegetation, aquatic or regularly flooded classes in the reporting period n
+- **Total mountain area** = Total area of mountains (in km2). In both the numerator and denominator, mountain area is defined according to UNEP-WCMC (2002).
+
+Overview of Sub-Indicator 15.4.2b (Proportion of degraded mountain land)
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+**Sub-indicator 15.4.2b**, Proportion of degraded mountain land, is designed to monitor the extent of degraded mountain land as a result of land cover change of a given country and for given reporting year. Similarly to sub-indicator ‘’trends in land cover” under SDG Indicator 15.3.1 (Sims et al. 2021), mountain ecosystem degradation and recovery is assessed based on the definition of land cover type transitions that constitute degradation, as either improving, stable or degraded. The definition of degradation adopted for the computation of this indicator is the one established Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES).
+
+.. math::
+
+ Proportion Of Degraded Mountain Land = (Degraded Mountain Area n) / (Total Mountain Area) * 100
+
+Where:
+
+- **Degraded mountain area n** = Total degraded mountain area (in km2) in the reporting period n. This is, the sum of the areas where land cover change is considered to constitute degradation from the baseline period. Degraded mountain land will be assessed based on the land cover transition matrix in Annex 1.
+- **Total mountain area** = Total area of mountains (in km2). In both the numerator and denominator, mountain area is defined according to UNEP-WCMC (2002).
+
+**Disaggregation:**
+
+Both of these sub-indicators are disaggregated by mountain bioclimatic belts as defined by Körner et al. (2011). In addition, sub-indicator 15.4.2a is
+disaggregated by the 10 SEEA classes based on UN Statistical Division (2014). Those values are reported both as proportions (percent) and area (in square kilometres)
+
+More detailed information on the overall conceptual framework of the indicator is available in the `indicator's metadata `_.
+
+Let’s us now compute SDG 15.4.2 step-by step using the example of Nepal.
+
+
+Defining the area of interest (AoI)
+-----------------------------------
+
+The calculation of the SDG 15.4.2 will be restricted to a specific area of interest defined by the user. In this first step, you will have the option to choose between a predefined list of administrative layers or to use a custom dataset.
+
+**1. Click on the Area of Interest Drawer to define your AoI.**
+
+A pop-up will display the available options to set your AoI:
+
+- Administrative definitions
+- Custom layers
+
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/Area_of_Interest.PNG
:align: center
- :width: 600
- :alt: Vegetation descriptor questionnaire
+ :width: 800
+ :alt: MGCI module
+**2. The Administrative definitions option uses the predifined administrative boundary layers available by default in the module. To define the Area of Interest using this option, do the following:**
-If you have selected **No**
-:::::::::::::::::::::::::::
+- Select **Country** under Administrative definitions.
+- In the dropdown list that will appear, select the country or territory in which you want to calculate SDG Indicator 15.4.2. In this example, we will select Nepal, as shown below.
-SEPAL-MGCI :sub:`beta` will use the ESA-CCI land cover dataset. You just have to select the year for which you want to compute the analysis (**select band/property** in the dropdown menu). Once you have selected the year, select :guilabel:`display on map` to create an IPCC land cover class.
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/Selecting_Nepal.PNG
+ :align: center
+ :width: 800
+ :alt: selecting_nepal
-.. image:: https://raw.githubusercontent.com/dfguerrerom/sepal_mgci/master/doc/img/3_default.PNG
+- Click on **Select Area of Interest (AOI)** and the map will display your selection. A corresponding legend is also displayed. The algorithm automatically generates a legend based on the mountain bioclimatic belt classes and the area for each of them as defined in the global mountain map developed by FAO to compute this indicator.
+
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/defining_aoi_customlayers.PNG
:align: center
- :width: 600
- :alt: Default classification
+ :width: 700
+ :alt: displaying_nepal
+
+.. warning:: The administrative boundaries available SEPAL-SDG 15.4.2 :sub:`beta` are extracted from FAO GAUL (Global Administrative Unit Layers) 2015 data set. The designations employed and the presentation of material on this map do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.
-If you have selected **Yes**
-::::::::::::::::::::::::::::
+**3. The Custom layers option allow users to use their own national administrative boundary layers. To define the Area of Interest using your own custom administrative boundary layer you have two options: use a vector file that you have previously uploaded in GEE as an asset (GEE asset name option), or use a vector file that you have previously uploaded in your SEPAL environment (Vector file option). To use a GEE asset, do the following:**
-Similarly to the mountain descriptor layer, to be able to use your own land cover map you would need upload it first to your GEE account or in a third-party account as a public asset (see `How to upload files to GEE `__). The dropdown menu will query all assets in your GEE folder that match the image type. You can select it from the dropdown list or directly copy and paste the link to the dataset.
+- Choose **GEE Asset Name** as your AOI selection method.
+- Copy the **Asset ID** in GEE and paste under "Select an asset"
+- Specify the column or leave the "Use all features" option to leave the default settings.
-.. image:: https://raw.githubusercontent.com/dfguerrerom/sepal_mgci/master/doc/img/3_custom.PNG
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/gee_asset_vector_selection.PNG
:align: center
:width: 600
- :alt: Custom classification
+ :alt: displaying_nepal
+
+Land cover dataset
+------------------
+
+In this section of the module, you have to indicate which land cover data you want to used in the analysis. If using land cover maps different from the default ones, you will also be requested to set up the land cover legend reclassification rules for Sub-indicator A and B, as well as the land cover transition matrix for computing Sub-Indicator B.
+
+Defining your land cover dataset to be used in the analysis
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+**1. Click on the Land cover dataset in the left panel menu.** A pop-up will ask you to indicate the land cover map you wish to use.
+
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/Land_cover_dataset_landing.PNG
+ :align: center
+ :width: 900
+ :alt: land cover module
+
+**2. In the first question of the questionnaire, you have to indicate the land cover maps that you wish to use to compute the indicator. If you want to use your own custom land cover datasets and select :guilabel:`yes` to this question, a new button (Open Parameters Settings) will appear. If you select :guilabel:`no`, the module will automatically use the default global land cover datasets for calculating this indicator (see section Data Sources above). Let's assume that you whish to your own land cover maps**.
+
+- Select :guilabel:`yes` to the first question. Then click on :guilabel:`Open Parameters Settings`
+
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/custom_dataset_subA.PNG
+ :align: center
+ :width: 800
+ :alt: land cover module
+
+- A new pop-up window will open to allow you to select your the collection of land cover maps as a GEE asset (remember that they must be stored as a `GEE image collection `_ to be able to be imported. Use the bottom arrow to choose your asset or copy/paste it directly from GEE. Then click on :guilabel:`Get classes`
+
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/importgeeimagecollection.PNG
+ :align: center
+ :width: 900
+ :alt: land cover module
-To allow SEPAL-MGCI :sub:`beta` to create an IPCC land cover class map using the land cover map you have provided, specify how the land cover classes of your map have to be reclassified into the :ref:`six IPCC classes ` in one of two ways:
+Reclassify the legend of your land cover map to compute sub-Indicator A
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-- Upload a table in .csv format (reclassification matrix), showing the IPCC land cover equivalent of the classes of your land cover map. See its structure in the :ref:`reclassification matrix ` section below. To provide information in this way, select :guilabel:`yes` below the question **Do you have a reclassification matrix table in .csv format?**
+- Once you have specified your custom land cover maps, you will be required to reclassify the legend of your land cover maps into the 10 landcover classes as defined by the UN-SEEA land cover classification, which is the default land cover legend for this sub-indicator.
- Once the table is in the **SEPAL enviroment**, select :guilabel:`Filename`, navigate through the folders, choose your table, and select the :guilabel:`load` button.
-
- .. image:: https://raw.githubusercontent.com/dfguerrerom/sepal_mgci/master/doc/img/3_search_table_and_load.PNG
- :align: center
- :width: 600
- :alt: Search and load table
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/reclass_sub_A.PNG
+ :align: center
+ :width: 900
+ :alt: reclass subA
+
+You can do this in two different ways:
+
+- Upload a reclassification matrix table in a csv format, indicating the SEEA land cover equivalent of the classes of your land cover map. To provide the information in this way, click on the arrow icon in the top right corner of the pop-up window. The table must already be uploaded in your SEPAL environment. To learn how to do that, please see the `how to exchange files in SEPAL `_. Note that the target values must match with the UN-SEAA classes codes for sub-indicator A (click on the info button at the top of the table for information on how the SEEA classes are codified into numbers).
- .. _reclass_table:
-
+.. _reclass_table:
.. tip:: What is a reclassification matrix table?:
- A reclassification matrix is a comma-separated values (.csv) file used to reclassify pixel values from one dataset into another. The .csv file only has to contain two values per line: the first one refers to the **from** value, while the second is the **target** value (see following table).
-
+
+ A reclassification matrix is a comma-separated values (CSV) file used to reclassify old pixel values into new ones. The CSV file only has to contain two values per line, the first one refers to the `from` value, while the second is the `target` value, just as it is described in the following table:
+
.. csv-table:: Reclassification table example
:header: "Origin class", "Target class"
:widths: auto
:align: center
-
+
"311", "1"
"111", "5"
"...","..."
"511", "4"
-
- To upload a classification table, see `How to exchange files in SEPAL `__.
-
- **Note**: The target values must match with the :ref:`IPCC classification table `.
-- Directly specify the reclassification rules by selecting :guilabel:`get table`; then, manually indicate the IPCC land cover equivalent (in the destination class column) of each of the land cover classes of your custom dataset (in the original class column) in the interactive table. To provide the reclassification matrix using this method, select **No** below the question, **Do you have a reclassification matrix table in .csv format?**
+- Directly specify the reclassification rules by manually indicating the SEEA land cover equivalent (in the destination class column) of each of the land cover classes of your land cover map (in the original class column). After manually reclassifying your legend, you can use the save button at the top of the table to store the table as a CSV file, and use it in a future calculation instead of manually filling up the table.
-.. image:: https://raw.githubusercontent.com/dfguerrerom/sepal_mgci/master/doc/img/3_1_reclassify_table.PNG
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/Reclassify_landcover.PNG
:align: center
- :width: 600
- :alt: Reclassification table
+ :width: 800
+ :alt: Reclassify table
+
+In our example, we will reclassify Nepal’s national land cover class using the following guide:
+
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/reclassification_guide_subA.PNG
+ :align: center
+ :width: 700
+ :alt: Reclassify table
+
+- Once you have reclassified all the land classes for Sub-Indicator A, click on "Reclassify Land Cover for Sub-Indicator B"
+
+Reclassify the legend of your land cover map to compute Sub-Indicator B
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+This step allows you to reclassify the legend of your land cover map for computing Sub-Indicator B.
+
+In contrast to Sub-Indicator A, the land cover legend used for the calculation of Sub-Indicator B does not necessarily have to be the 10 UN-SEEA classes mentioned earlier. In this sub-indicator, the UN-SEEA legend can be adapted to the national context to ensure that it adequately captures the key degradation and improvement transitions identified in the prior step. For instance, a given country may decide to differentiate "natural forests" from "tree plantations" in sub-indicator B.
+
+For this reason, this step allows users to apply a new reclassification, or alternatively, used the same reclassification rules as in Sub-Indicator A. In the latter case. In any of both cases, users will need to upload the land cover reclassification rules in a csv file, following the same method as in the prior step.
+
+Upload a transition matrix for computing Sub-Indicator B
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+This step should only be completed if you have prodivded different land cover reclassification rules for Sub-Indicator B in the prior step. In such a case, in this step you will need to upload a land cover transition matrix, defining which land cover transitions are considered to be “degradation” and “improvement”, consistent to the legend you have provided in the prior step. This will allow SEPAL-SDG 15.4.2 :sub:`beta` to compute this sub-indicator in the next processing steps.
+
+Here again the transition matrix should have been previously uploaded in your SEPAL environment as a csv file, containing the following columns: from_code, to_code, impact_code, columns names have to be exactly the same.
+
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/4_transition_matrix.PNG
+ :align: center
+ :width: 700
+ :alt: Reclassify table
+
+Changing the default land cover transition matrix for computing Sub-Indicator B using the default global land cover data
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+SEPAL-SDG 15.4.2 :sub:`beta` allows the user to modify the default land cover transition matrix without needing to provide a custom land cover map. This allow national authorities to adapt the transition matrix to to the local context and in this way better capture the main land degradation processes occurring in the country without needing to provide alternative land cover data.
+
+This can be done selecting :guilabel:`Yes` in the second question of the land cover dataset questionnaire, and then clicking on "Open Parameter Settings".
-.. tip:: After manually reclassifying your dataset, use the :guilabel:`save` button to store the table as a .csv file so that it can be used again later.
-
-Display results
-^^^^^^^^^^^^^^^
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/Yes_to_second_question.PNG
+ :align: center
+ :width: 800
+ :alt: Reclassify table
+
+This will open a pop-up window including the default land cover transitions matrix, showing positive land cover transitions in green, negative in red, and stable/neutral transitions in blue. The matrix can be directly modified by clicking on each cell and changing the sign of the transition.
+
+
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/Modify_default_transitions.PNG
+ :align: center
+ :width: 900
+ :alt: Reclassify table
+
+Once finished, just click outside the window and move to the next processing step: Indicators Settings.
+
+.. note::
+
+ Adapting the default land cover transition matrix using the default global land cover data should be carefully considered. Decisions about which land cover transitions are linked to a degradation or an improvement process in the context of sub-indicator B should be made taking into account the expected change in biodiversity and the mountain ecosystem functions or services that are considered desirable in a local or national context. For these reasons, FAO recommends to consider as degradation all land cover transitions that involve changes from natural land cover types (such as forests, shrublands, grasslands, wetlands) to anthropogenic land cover types (artificial surfaces, cropland, pastures, plantation forests, etc.) as a general rule, given that land use change is known to be the primary driver of biodiversity loss (IPBES, 2019).
+
+Indicators settings
+-------------------
-Once you have reclassified the new values or used the default land cover dataset, display the reclassified map by selecting the :guilabel:`display map` button. Depending on your AOI, the map should look like this:
+Now that we have defined our area of interest and the land cover data to be used in the analysis, together with the land cover legend reclassification rules and associated transitions matrix, click on the **Indicator Settings drawer** to start setting the parameters that the tool will need in the computation of the sub-indicators.
-.. image:: https://raw.githubusercontent.com/dfguerrerom/sepal_mgci/master/doc/img/3_3_vegetation_descriptor_2.PNG
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/Indicator_settings.PNG
+ :align: center
+ :width: 900
+ :alt: Reclassify table
+
+From here on, let’s tackle the sub-indicators individually.
+
+Defining parameters for Sub-indicator A: Mountain Green Cover Index
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+**1. Click on the add layer icon (highlighted below) to define the years for which the indicator will be calculated**
+
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/sub_indicatorA.PNG
:align: center
:width: 600
- :alt: Vegetation layer example map
+ :alt: Reclassify table
+
+**2. In the pop-up window that will appear you need to link each of the land maps (either the default ones or the custom ones that you may have uploaded in the prior steps) to the corresponding reference year of each map. You can report one or multiple years. To increase the number of years to be reported, just click on the + sign to define additional years that you need to report.**
+
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/sub_a_reporting_years.PNG
+ :align: center
+ :width: 500
+ :alt: Reclassify table
+
+.. note::
+
+ Remember that reporting years for Sub-indicator A are 2000, 2005, 2010, 2015 and subsequently every 3 years (2018, 2021, 2024,...). If you are using custom national land cover maps that are not annually updated and does not exactely match reporting years (for example, you may have a land cover map for 2004 instead of 2005), the tool will automatically interpolates values for the reporting years based on the years for which land cover data is available.
-.. tip::
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/defining_multiple_years.PNG
+ :align: center
+ :width: 400
+ :alt: Reclassify table
+
+**3. When finished, press OK. The list of reporting years will now be listed at the bottom of the Sub-Indicator A box.**
+
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/defining_multiple_results.PNG
+ :align: center
+ :width: 500
+ :alt: Reclassify table
- Remember that the MGCI is only calculated over mountain classes, so the vegetation layer will mask out the areas where there is no presence of a mountain class.
+Defining parameters for Sub-Indicator B: Proportion of Degraded Mountain Land.
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+In contrast to Sub-Indicator A, in Sub-Indicator B the extent of degraded mountain land is calculated first in the baseline period 2000 - 2015. This baseline sets the benchmark from which the extent of land degradation is measured and monitored every 3 years after 2015. Put simply, new land cover degradation in the reporting periods (2018, 2021, 2024, ...) is added to the baseline to estimate the current extent of land cover degradation. This is why in this instance the tool automatically uses the 2000-2015 as baseline.
-MGCI calculation
-----------------
+**1. Define your landcover maps for the baseline years (2000 and 2015) by linking each of the land maps to the corresponding reference year of each map. If you are using custom national land cover maps that does not exactely match reporting years of the baseline, select the map whose reference year is closest to the reporting year (For example, you could select a land cover map for 1998 for the reporting year 2000).**
-Once you have set the inputs in the previous steps, select **Calculate MGCI** to calculate both the area of each IPCC land cover class and MGCI values for the whole mountain area and for each mountain class. The module has the option of executing the calculation using the planimetric area or the `real surface area `__. Each section will provide an overall MGCI displayed in a circle, along with the summary of the area in each of the IPCC classes, as shown in the below image.
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/sub_B_baseline.PNG
+ :align: center
+ :width: 500
+ :alt: Reclassify table
-.. _calculation:
+**2. Then define the land cover maps for each of the reporting years and click OK**
-Calculation
-^^^^^^^^^^^
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/subB_reporting_years.PNG
+ :align: center
+ :width: 500
+ :alt: Reclassify table
-Depending on the size of your AOI and whether you are using the real surface area or not, the process could take longer. As explained in the previous sections, the calculation of the land cover/use area per mountain class, as well as the MGCI, is done in GEE, meaning that the computation is restricted by the available GEE resources; one of these limitations is the time to get the results on the fly (see `Computation time out `__).
+Calculation of SDG Indicator 15.4.2
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+Once you have set the parameters of each sub-indicator, the tool is now ready to run as shows below:
-.. image:: https://raw.githubusercontent.com/dfguerrerom/sepal_mgci/master/doc/img/4_dashboard_1_calculation.PNG
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/calculate_mgci.PNG
:align: center
:width: 600
- :alt: Dashboard calculation
+ :alt: Reclassify table
-To overcome this limitation, the process will be executed as a task, which is an operation that is capable of running much longer than the standard timeout (see `GEE tasks `__). If the computation is created as a task, you will see a similar message as shown in the following image. To receive the results, see the :ref:`calculation from task ` section; otherwise, the result will be displayed on the dashboard (see :ref:`dashboard `).
+**1. Click on the "Calculate MGCI" to initiate the computation.**
-.. image:: https://raw.githubusercontent.com/dfguerrerom/sepal_mgci/master/doc/img/4_computation_timeout.PNG
+**2. Once is completed, you should see something like the image below:**
+
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/MGCI_done..PNG
:align: center
:width: 600
- :alt: Computation timed out
+ :alt: Reclassify table
-.. _calculation_from_task:
+.. tip::
-Calculation from task
-^^^^^^^^^^^^^^^^^^^^^
+ SEPAL-SDG 15.4.2 :sub:`beta` calculates the indicator values assuming a planimetric area methods by default. To calculate indicator values using a real surface area method (a method that takes into account the third dimension of mountain surfaces through the use of digital elevation models and is known to derive closer estimates of the real surface area of mountain regions), click on "Use Real Surface Area"
-If the computation can't be done on the fly, a new file containing the ID of the task is created and stored in the `../module_results/sdg_indicators/mgci/tasks` folder, which will help you track the status of the task. To do so, search for this file in your SEPAL environment using the **Navigator** by selecting the :guilabel:`search file` button; then, select the :guilabel:`Calculate MGCI` button. The result will be displayed if the process status is complete.
+3. The entire process is done "on the fly” and thus you need to export your reporting tables to visualize and use them when required. To do that, click on the "Export Reporting Tables". When completed, a message will appear indicating where the tables have been exported.
-.. tip::
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/Export_Tables.PNG
+ :align: center
+ :width: 600
+ :alt: Reclassify table
- An alternative way to track the progress of the task is by using the `GEE task tracker `_, where you can find tasks running on the server.
+Calculation from Task
+^^^^^^^^^^^^^^^^^^^^^
+As explained in the previous sections, SEPAL runs on GEE. This means that the computation is restricted by the GEE available resources. One of these limitations is the time to get the results on the fly (see `computation time out `_). So any computation that takes more than five minutes will throw an exception. To overcome this limitation, the process will be executed as a task —which are operations that are capable of running much longer than the standard timeout. If the computation is created as a task, you will see a similar message as the shown in the below image.
-.. image:: https://raw.githubusercontent.com/dfguerrerom/sepal_mgci/master/doc/img/4_dashboard_tasks.PNG
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/tasks_notice.png
:align: center
:width: 600
- :alt: Download from task
-
-|
+ :alt: Reclassify table
-.. _display:
+When computation can’t be done on the fly, a new file containing the id of the task is created and stored in the ../module_results/sdg_indicators/mgci/tasks folder. This file will help you to track the status of the task at any moment. An alternative way to track the progress of the task is by using the GEE task tracker, there you can find the tasks that are running on the server.
-Display dashboard
-^^^^^^^^^^^^^^^^^
+**1. To enable a computation from task; first we need to locate the tasks file within SEPAL.**
+
+To do so, you only have to search this file in your SEPAL environment using the navigator by clicking on the search file button, and then clicking over the Calculate MGCI button and the result will be displayed if the process status is completed. To locate the tasks manually, alternatively to locate the tasks navigate to the File Layer > Downloads > Module results>Tasks on SEPAL as shown below.
+
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/locating_tasks.PNG
+ :align: center
+ :width: 700
+ :alt: Reclassify table
+
+**2. Once that’s done in GEE, you will need to bring it back to SEPAL for the tool to finish computation. Click on the "Calculation from Task" tab to initiate this process.**
+
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/calculation_from_task.PNG
+ :align: center
+ :width: 700
+ :alt: Reclassify table
+
+**3. Load your task to finish computation.**
+
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/task_file_choice.PNG
+ :align: center
+ :width: 700
+ :alt: Reclassify table
+
+Visualizing the results
+-----------------------
-Whether the computation is done on the fly or you have used the task, the dashboard will be rendered in the same way (i.e. divided into two sections):
+We can visualize the results in the following two ways:
-- Overall MGCI: Indicates the overall index for the whole mountain area.
-- Mountain class MGCI: Indicates the index for that specific mountain class.
+• The exported tables: These provide the full results of the computation in a tabular format.
-.. note:: The module will only work with the six IPCC classes. If you have provided different values to the classes, the module will classify them as the **Other lands** class (IPCC 6).
+• Using the MGCI results drawer provides a simplified and visual representation of the results.
-Export results
-^^^^^^^^^^^^^^
+Let’s look at these individually.
-After the calculation is done, the **Export** button will become available. To generate the report, enter your institution's name and select :guilabel:`export reporting tables` for the year of the land use/cover map. The report will consist of the following three files:
+Exporting tables
+^^^^^^^^^^^^^^^^
-- ER_MTN_GRNCOV: Mountain green cover area (skqm).
-- ER_MTN_GRNCVI: Mountain Green Cover Index.
-- ER_MTN_TOTL: Total mountain area (sqkm)
+As explained earlier, once computation is completed, users can export the reporting tables to their SEPAL environment
-.. image:: https://raw.githubusercontent.com/dfguerrerom/sepal_mgci/master/doc/img/4_dashboard_export.PNG
+**1. To locate the tables, navigate to the Files Tab > Under the Downloads, you should see your table under MGCI reports as shown below:**
+
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/downloading_report.PNG
:align: center
- :width: 600
- :alt: Export report
+ :width: 700
+ :alt: Reclassify table
+
+**2. To download the report from SEPAL, click on the report and this activates the download icon in the top right side of the screen.**
+
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/export_mgci.PNG
+ :align: center
+ :width: 700
+ :alt: Reclassify table
+
+**3. Once the report is downloaded, you can visualize the results of the computation as seen below for all the reporting years defined earlier on.**
+
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/results_excel_subA.PNG
+ :align: center
+ :width: 700
+ :alt: Reclassify table
+
+The tables follow the standard format for SDG reporting and therefore can be used to report SDG Indicator 15.4.2 values to FAO
+
+Visualizing the results through the MGCI Results Drawer
+^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
+
+SEPAL-SDG 15.4.2 :sub:`beta` also allows to explore the results of the computation visually. The module generates dashboards that show the changes that have occurred in the area of interest. To generate these dashboards do the following;
-Once the process is done, the alert message will display the storage location of the report files, which can be downloaded by using any of the options presented in `Exchange files in SEPAL `__.
+**1. Click on the **MGCI results drawer** in the left panel. To see the results from the computation for Sub-Indicator A, choose which year you want to visualize and click on the Calculate button.
+
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/MGCI_Results_SUbA.PNG
+ :align: center
+ :width: 700
+ :alt: Reclassify table
+
+This generates dashboards to visualize the results of the computation. As seen below, the tool will generate an Overall MGCI for your study area. Additionally, a dashboard will be generated for each of the bioclimatic classes.
+
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/Visualizing%20SUbA.PNG
+ :align: center
+ :width: 700
+ :alt: Reclassify table
+
+**2. To see the results for Sub-Indicator B, choose a target year (baseline or one of the reporting years) using the drop-down arrow and a bioclimatic belt. Then click on Calculate:**
+
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/results_sub_indicator_b.PNG
+ :align: center
+ :width: 700
+ :alt: Reclassify table
+
+The results, shown as transitions in land cover types for a given belt will be displayed using a Sankey Plot, as shown below:
+
+.. image:: https://raw.githubusercontent.com/xavidelamo/sepal_images/main/computation/nival_results.PNG
+ :align: center
+ :width: 700
+ :alt: Reclassify table
.. custom-edit:: https://raw.githubusercontent.com/sepal-contrib/sepal_mgci/release/doc/en.rst
diff --git a/docs/source/modules/dwn/sepal_pysmm.rst b/docs/source/modules/dwn/sepal_pysmm.rst
index 6488cd3375..55201e22b0 100644
--- a/docs/source/modules/dwn/sepal_pysmm.rst
+++ b/docs/source/modules/dwn/sepal_pysmm.rst
@@ -1,12 +1,14 @@
Soil moisture mapping
=====================
+*Map surface soil moisture based on Copernicus Sentinel-1 intensity data*
Open SEPAL
----------
+*Tool for mapping surface soil moisture based on Copernicus Sentinel-1 intensity data*
#. Open SEPAL and log in.
- #. To open SEPAL in your browser, go to ``_
+ #. To open SEPAL in your browser, go to ``_.
#. Connect SEPAL to your Google account.
#. Make sure SEPAL is connected to your Google account (see `Use GEE with SEPAL `_).
@@ -22,17 +24,17 @@ Process Sentinel-1 time series data to generate maps of soil moisture
#. Open and launch the **Soil moisture mapping** application.
- #. To access the module, select the **Apps** tab in SEPAL. Then use the search box and enter “SOIL MOISTURE MAPPING” or find it manually.
+ #. To access the module, select the **Apps** tab in SEPAL. Use the search box and enter “SOIL MOISTURE MAPPING”, or find it manually.
.. figure:: https://raw.githubusercontent.com/openforis/sepal_pysmm/master/doc/img/wiki/2.1.1.PNG
:width: 500
- #. The application will be launched and displayed over a new tab in the SEPAL panel.
+ #. The application will be launched and displayed over a new tab in the SEPAL pane.
.. figure:: https://raw.githubusercontent.com/openforis/sepal_pysmm/master/doc/img/wiki/2.1.2.PNG
:width: 500
- #. The module has 5 main steps that can be selected in the left panel: **AOI selection**, **download**, **closing filter**, **calculate statistics**, and **display map**.
+ #. The module has five main steps that can be selected in the left pane: **AOI selection**, **download**, **closing filter**, **calculate statistics**, and **display map**.
#. Select the **AOI selection** step and follow the next four sub-steps.
#. In the **AOI selection step**, choose **Use GEE asset**. Paste your **GEE asset ID** into the box and select the “Use asset” button to select your AOI.
#. Two new selection dropdown menus will appear. Choose your **variable** and **field**, then wait until the polygon is loaded onto the map.
@@ -56,10 +58,10 @@ Process Sentinel-1 time series data to generate maps of soil moisture
#. This process could take a long time depending on the dimensions of the feature and the number of images to be processed.
#. If the selected dates are not available, the system will display a message with the closest images to the input dates.
- #. The most recent image available depends on the GLDAS product, which has a delay of one to two months.
+ #. The most recent image available depends on the Global Land Data Assimilation System (GLDAS) product, which has a delay of one to two months.
#. The green **Processing** bar shows the name of the task that is sent to GEE. When the processing reaches 100 percent, all tasks have been sent to GEE and the classification of soil moisture will continue there.
- #. After all tasks are sent to GEE, the module can be closed. The processing will continue uninterrupted in GEE, where the processing can take hours or days depending on the size of the AOI and the date range selected.
+ #. After all tasks are sent to GEE, the module can be closed. The processing will continue uninterrupted in GEE, where it can take hours or days depending on the size of the AOI and the date range selected.
.. figure:: https://raw.githubusercontent.com/openforis/sepal_pysmm/master/doc/img/wiki/2.3.6.PNG
:width: 500
@@ -93,7 +95,7 @@ Download soil moisture maps from GEE to SEPAL
#. Use the download step.
- #. In the left panel, select the **Download** button.
+ #. In the left pane, select the **Download** button.
.. figure:: https://raw.githubusercontent.com/openforis/sepal_pysmm/master/doc/img/wiki/3.2.1.PNG
:width: 180
@@ -112,7 +114,7 @@ Download soil moisture maps from GEE to SEPAL
#. The task download file can be found in the folder :code:`home/user/ pysmm_downloads/0_raw/assetname/rowname/`
#. The task download file naming convention is: task_datedownloadinitiated_code.txt
#. Use the three dropdown lists to choose the desired task text file by selecting the folder names.
- #. There are options to overwrite duplicates already downloaded into SEPAL and remove downloaded images from Google Drive. Once the images are removed from Google Drive the task download file will no longer function because those images will not be stored in Google Drive.
+ #. There are options to overwrite duplicates already downloaded into SEPAL and remove downloaded images from Google Drive. Once the images are removed from Google Drive, the task download file will no longer function because those images will not be stored in Google Drive.
#. **Overwrite SEPAL images**: In case you previously have downloaded an image in the same path folder, the module will overwrite the images with the same name.
#. **Remove Google Drive images**: Mark this option if you want to download the images to your SEPAL account and delete the files from your Google Drive account.
@@ -121,19 +123,19 @@ Download soil moisture maps from GEE to SEPAL
#. The images will download separately; leave the application open while the download is running.
#. After the data download is complete, you can use tools available in SEPAL to process and analyse the soil moisture maps.
-Post-process and analyse soil moisture time-series data
+Post-process and analyse soil moisture time series data
-------------------------------------------------------
After the download is complete, apply a robust methodology for image filtering to fill no-data gaps and assess trends in the time series of soil moisture maps.
#. Select the **Closing filter** step.
- #. In the left panel, select the **Closing filter** tab.
+ #. In the left pane, select the **Closing filter** tab.
.. figure:: https://raw.githubusercontent.com/openforis/sepal_pysmm/master/doc/img/wiki/4.1.1.PNG
:width: 180
-#. Run the post-processing section of the module
+#. Run the post-processing section of the module.
#. Navigate to the folder where the images are stored. This module will process a folder with many images, going through each of the images. Therefore, the input should be the folder in which the raw images are stored. The module will automatically display two selection menus; select the desired options.
@@ -142,12 +144,12 @@ After the download is complete, apply a robust methodology for image filtering t
#. The raw imagery is stored in the same folder that the task download file is located.
#. Select the **START** button to run a data-filling algorithm on each of the soil moisture maps.
- #. Due to speckle in Sentinel-1 imagery, soil moisture maps contain some noise and no-data values which are corrected to some extent using grayscale morphological operation from ORFEO toolbox, a free and open-source image processing tool. To read more about the parameterization of the Orfeo toolbox tool, see ``_
- #. This process is done by the SEPAL instance; the time will depend on the number of images and dimensions. After finishing all images, the progress bar will turn green.
+ #. Due to speckle in Sentinel-1 imagery, soil moisture maps contain some noise and no-data values which are corrected to some extent using grayscale morphological operation from ORFEO toolbox, a free and open-source image processing tool (see ``_.
+ #. This process is done by the SEPAL instance; the time will depend on the number of images and dimensions. After finishing all images, the **Progress** bar will turn green.
-#. Run the **Statistics** postprocess.
+#. Run the **Statistics** post-process.
- #. In the left panel select the **Calculate statistics** tab.
+ #. In the left pane, select the **Calculate statistics** tab.
.. figure:: https://raw.githubusercontent.com/openforis/sepal_pysmm/master/doc/img/wiki/4.3.1.PNG
:width: 180
@@ -181,7 +183,7 @@ After the download is complete, apply a robust methodology for image filtering t
#. The **Median**, **Mean**, **Geometric Mean**, **Max**, **Min**, **Standard Deviation** and **Valid pixels** are statistics that do not require much computing requirements, so the time to perform those tasks is relatively quick, depending on the extent of the image.
#. The **Advanced settings** are intended to be used to improve the time and manage system resources. Normally, this is automatically optimized, but can be modified by the user. This setting controls the number of processors you use for parallel processing, allowing you to optimize the time by processing a huge image by using several processors at the same time (by default, all available processors will be used; note that the more CPUs available in the selected instance in the terminal, the faster the processing will be).
- #. **Processors**: By default, the module will display the number of processors that are active in the current instance session and will perform the stack-composed with all of them; however, in order to test the best benchmark to the specific stack, this number could be changed within the **Advanced settings** tab.
+ #. **Processors**: By default, the module will display the number of processors that are active in the current instance session and will perform the stack composed with all of them; however, in order to test the best benchmark to the specific stack, this number could be changed within the **Advanced settings** tab.
#. **Chunks**: The number in the chunk specifies the shape of the array that will be processed in parallel over the different processors (i.e. if 180 is the specified number of chunks, then the stack-composed module will divide the input image into several small square pieces of 180 pixels with its shape). For more information about how to select the best chunk shape, follow the documentation.
.. figure:: https://raw.githubusercontent.com/openforis/sepal_pysmm/master/doc/img/wiki/4.3.5.7.PNG
@@ -198,7 +200,7 @@ After the download is complete, apply a robust methodology for image filtering t
Visualizing imagery
-------------------
-#. In the left panel, select the **Display map** tab.
+#. In the left pane, select the **Display map** tab.
.. figure:: https://raw.githubusercontent.com/openforis/sepal_pysmm/master/doc/img/wiki/5.1_.PNG
:width: 180
@@ -219,7 +221,7 @@ Visualizing imagery
.. figure:: https://raw.githubusercontent.com/openforis/sepal_pysmm/master/doc/img/wiki/5.4.PNG
:width: 500
-Open-source data from Sentinel-1 operates using C-band synthetic aperture radar imaging. C-band type has a wavelength of 3.8 cm – 7.5 cm, and thus has limited penetration into dense forest canopies. Therefore, forested areas should be excluded from the analysis. L-band data should be used instead of such areas.
+Open-source data from Sentinel-1 operates using C-band synthetic aperture radar imaging. C-band type has a wavelength of 3.8–7.5 cm, and thus has limited penetration into dense forest canopies. Therefore, forested areas should be excluded from the analysis. L-band data should be used instead of such areas.
It is recommended that densely vegetated areas are excluded from analysis due to the limitation of C-band radar to penetrate dense canopy cover. Use a **forest map** to exclude dense forest areas from the analysis.
diff --git a/docs/source/modules/dwn/seplan.rst b/docs/source/modules/dwn/seplan.rst
index 740d872b66..2b2bc81a8e 100644
--- a/docs/source/modules/dwn/seplan.rst
+++ b/docs/source/modules/dwn/seplan.rst
@@ -1,35 +1,51 @@
se.plan
=======
+*Generate information on forest restoration potential to support forest restoration planning decisions with se.plan*
+
Introduction
------------
Overview
^^^^^^^^
-**se.plan** is a spatially explicit online tool designed to support forest restoration planning decisions by restoration stakeholders. It is part of `SEPAL `_ (System for Earth Observation Data Access, Processing and Analysis for Land Monitoring), a component of UN FAO’s free, open-source software suite, `Open Foris `_. It aims to identify locations where the benefits of forest restoration are high relative to restoration costs, subject to biophysical and socioeconomic constraints that users impose to define the areas where restoration is allowable. The computation is performed using cloud-based supercomputing and geospatial datasets from Google Earth Engine available through SEPAL. As a decision-support tool, it is intended to be used in combination with other information users may have that provides greater detail on planning areas and features of those areas that **se.plan** might not adequately include. It offers users the option to replace its built-in data layers, which are based on publicly available global datasets, with users’ own customized layers. Please see :ref:`seplan-appendix-a` for a list of **se.plan**’s built-in data layers and their sources.
+**se.plan** is a spatially explicit online tool designed to support forest restoration planning decisions by restoration stakeholders.
+
+Part of `System for Earth Observation Data Access, Processing and Analysis for Land Monitoring (SEPAL) `_, a component of the free, open-source software suite, `Open Foris `_ from the Food and Agriculture Organization of the United Nations (FAO), **se.plan** aims to identify locations where the benefits of forest restoration are high relative to restoration costs, subject to biophysical and socioeconomic constraints that users impose to define the areas where restoration is allowable. The computation is performed using cloud-based supercomputing and geospatial datasets from Google Earth Engine (GEE) available through SEPAL.
+
+As a decision-support tool, **se.plan** is intended to be used in combination with other information users may have that provides greater detail on planning areas and features of those areas that the tool might not adequately include. It offers users the option to replace its built-in data layers, which are based on publicly available global datasets, with users’ own customized layers (see :ref:`seplan-appendix-a` for a list of the tool’s built-in data layers and their sources).
-The sections below highlight key features of **se.plan**. In brief, following the three steps below, se.plan can be used to generate information on forest restoration potential.
+The following sections highlight key features of **se.plan**.
+
+Following the three steps below, the tool can be used to generate information on forest restoration potential.
.. rst-class:: center
- Users start by (i) selecting their geographical planning area, (ii) rating the relative importance of different restoration benefits from their perspective, and (iii) imposing constraints that limit restoration to only those sites they view as suitable, in view of ecological and socioeconomic risks. **se.plan** then generates maps and related information on restoration’s benefits, costs, and risks for all suitable sites within the planning area.
+To generate maps and related information on restoration’s benefits, costs and risks for all suitable sites within the planning area:
+
+ 1. Select your geographical planning area.
-In addition to reading this manual, we encourage users to watch **se.plan** YouTube videos:
+ 2. Rate the relative importance of different restoration benefits from your perspective.
+
+ 3. Impose constraints that limit restoration to only those sites viewed as suitable (related to ecological and socioeconomic risks).
+
+In addition to reading this article, the SEPAL team encourages users to watch the following video:
.. youtube:: 37pCFhF4zBI
Geographical resolution and scope
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
-**se.plan** divides the Earth’s surface into grid cells with 30 arc-second resolution (≈1km at the equator). It includes only grid cells that satisfy the following four criteria (`view in gee `__)):
+**se.plan** divides the Earth’s surface into grid cells with 30 arc-second resolution (approximately 1 km at the equator).
+
+The tool only includes grid cells that satisfy the following four criteria (`view in GEE `__):
-- They are in countries or territories of Africa and the Near East, Asia and the Pacific, and Latin America and the Caribbean that the World Bank classified as *low or middle-income countries* or territories (LMICs) during most years during 2000–2020. These countries and territories number 139 and are listed in :ref:`seplan-appendix-b`.
+- They are in countries or territories of Africa and the Near East, Asia and the Pacific, and Latin America and the Caribbean that the World Bank classifies as *low- or middle-income countries* or territories (LMICs) during most years of the 2000–2020 period. There are 139 countries and territories in total and can be found in the section, :ref:`seplan-appendix-b`.
- They include areas where tree cover can potentially occur under current climatic conditions, as determined by `Bastin et al. (2019) `_.
- Their current tree cover, as measured by the European Space Agency’s Copernicus Programme (`Buchhorn et al. 2020 `_), is less than their potential tree cover.
- They are not in urban use.
-**se.plan** labels grid cells that satisfy these criteria potential restoration sites. It treats each grid cell as an independent restoration planning unit, with its own potential to provide restoration benefits and to entail restoration costs and risks.
+**se.plan** labels grid cells that satisfy these criteria as potential restoration sites. It treats each grid cell as an independent restoration planning unit, with its own potential to provide restoration benefits and entail restoration costs and risks.
Methodology
-----------
@@ -37,76 +53,79 @@ Methodology
Selection of planning area
^^^^^^^^^^^^^^^^^^^^^^^^^^
-**se.plan** offers users multiple ways to select their planning area, which **se.plan** labels as *Area Of Interest* (AOI) as described in the :ref:`seplan-usage`.
+**se.plan** offers users multiple ways to select a planning area, which the tool labels as **Area of interest (AOI)** as described in the :ref:`seplan-usage`.
Restoration benefits
^^^^^^^^^^^^^^^^^^^^
-Restoration offers many potential benefits. In its current form, **se.plan** provides information on four benefits:
+In its current form, **se.plan** provides information on four benefits:
-- Biodiversity conservation
-- Carbon sequestration
-- Local livelihoods
-- Wood production
+- biodiversity conservation
+- carbon sequestration
+- local livelihoods
+- wood production
-**se.plan** includes two indicators each for biodiversity conservation and local livelihoods and one indicator each for carbon sequestration and wood production. Each indicator is associated with a data layer that estimates each grid cell’s relative potential to provide each benefit if the grid cell is restored; the relative potential is measured on a scale of 1 (low) to 5 (high). Please see :ref:`seplan-appendix-c` for more detail on the interpretation and generation of the data layers for the benefit indicators.
+**se.plan** includes two indicators each for biodiversity conservation and local livelihoods, and one indicator each for carbon sequestration and wood production. Each indicator is associated with a data layer that estimates each grid cell’s relative potential to provide each benefit if the grid cell is restored; the relative potential is measured on a scale of 1 (low) to 5 (high) (see :ref:`seplan-appendix-c` for more information on the interpretation and generation of data layers for the benefit indicators).
-Users rate the relative importance of these benefits from their standpoint (or the standpoint of stakeholders they represent), and **se.plan** then calculates an index that indicates each grid cell’s relative restoration value aggregated across all four benefit categories. This restoration value index is a weighted average of the benefits, with user ratings serving as the weights. It therefore accounts for not only the potential of a grid cell to provide each benefit but also the relative importance that a user assigns to each benefit. It is scaled from 1 (low restoration value) to 5 (high restoration value). Please see :ref:`seplan-appendix-d` for more detail on the generation of the index.
+Users rate the relative importance of these benefits from their standpoint (or the standpoint of stakeholders they represent). Then, **se.plan** calculates an index that indicates each grid cell’s relative restoration value aggregated across all four benefit categories. This restoration value index is a weighted average of the benefits with user ratings serving as the weights. It therefore accounts for not only the potential of a grid cell to provide each benefit, but also the relative importance that a user assigns to each benefit. It is scaled from 1 (low restoration value) to 5 (high restoration value) (see :ref:`seplan-appendix-d` for more information on the generation of the index).
Restoration cost
^^^^^^^^^^^^^^^^
Forest restoration incurs two broad categories of costs, **opportunity cost** and **implementation costs**.
-**Opportunity cost** refers to the value of land if it is not restored to forest. **se.plan** assumes that the alternative land use would be some form of agriculture, either cropland or pasture. It sets the opportunity cost of potential restoration sites equal to the value of cropland for all sites where crops can be grown, with the opportunity cost for any remaining sites set equal to the value of pasture. Sites that cannot be used as either cropland or pasture are assigned an opportunity cost of zero.
+**Opportunity cost** refers to the value of land if it is not restored to forest. **se.plan** assumes that the alternative land use would be some form of agriculture (either cropland or pasture). It sets the opportunity cost of potential restoration sites equal to the value of cropland for all sites where crops can be grown, with the opportunity cost for any remaining sites set equal to the value of pasture. Sites that cannot be used as either cropland or pasture are assigned an opportunity cost of zero.
-**Implementation costs** refer to the expense of activities required to regenerate forests on cleared land. They include both: (i) initial expenses incurred in the first year of restoration (establishment costs), which are associated with such activities as site preparation, planting, and fencing; and (ii) expenses associated with monitoring, protection, and other activities during the subsequent 3–5 years that are required to enable the regenerated stand to reach the “free to grow” stage (operating costs).
+**Implementation costs** refer to the expense of activities required to regenerate forests on cleared land, including both:
-**se.plan** assumes that implementation costs include planting expenses on all sites. This assumption might not be valid on sites where natural regeneration is feasible. To account for this possibility, **se.plan** includes a data layer that predicts the variability of natural regeneration success.
+ 1. initial expenses incurred in the first year of restoration (**Establishment costs**), which are associated with such activities as site preparation, planting and fencing; and
+ 2. expenses associated with monitoring, protection and other activities during the subsequent three to five years that are required to enable the regenerated stand to reach the “free to grow” stage (**Operating costs**).
-**se.plan** calculates the overall restoration cost of each site by summing the corresponding estimates of the opportunity cost and implementation costs. Please see :ref:`seplan-appendix-e` for more detail on the interpretation and generation of the data layers for opportunity and implementation costs.
+**se.plan** assumes that implementation costs include planting expenses on all sites. This assumption might not be valid on sites where natural regeneration is feasible. To account for this possibility, the tool includes a data layer that predicts the variability of natural regeneration success.
-Benefit-cost ratio
+**se.plan** calculates the overall restoration cost of each site by combining the corresponding estimates of the **Opportunity cost** and **Implementation costs** (see :ref:`seplan-appendix-e` for more information on the interpretation and generation of the data layers for opportunity and implementation costs).
+
+Benefit–cost ratio
^^^^^^^^^^^^^^^^^^
-**se.plan** calculates an approximate benefit-cost ratio for each site by dividing the restoration value index by the restoration cost and converting the resulting number to a scale from 1 (small ratio) to 5 (large ratio). Sites with a higher ratio are the ones that **se.plan** predicts are more suitable for restoration, subject to additional investigation that draws on other information users have on the sites. Please see :ref:`seplan-appendix-d` for more detail on the generation and interpretation of this ratio. A key limitation is that the ratio does not account interdependencies across sites related to either benefits, such as the impact of habitat scale on species extinction risk, or costs, such as scale economies in planting trees. This limitation stems from **se.plan**’s treatment of each potential restoration site as an independent restoration planning unit.
+**se.plan** calculates an approximate benefit–cost ratio for each site by dividing the restoration value index by the restoration cost and converting the resulting number to a scale from 1 (small ratio) to 5 (large ratio). Sites with a higher ratio are the ones that the tool predicts are more suitable for restoration, subject to additional investigation that draws on other information users have on the sites (see :ref:`seplan-appendix-d` for more information on the generation and interpretation of this ratio).
+
+A key limitation is that the ratio does not account for interdependencies across sites related to either benefits, such as the impact of habitat scale on species extinction risk, or costs, such as scale economies in planting trees. This limitation stems from the tool’s treatment of each potential restoration site as an independent restoration planning unit.
Constraint
^^^^^^^^^^
**se.plan** allows users to impose constraints that limit restoration to only those sites they view as suitable, in view of ecological and socioeconomic risks. It groups the constraints into four categories:
-- Biophysical (5 constraints): elevation, slope, annual rainfall, baseline water stress, terrestrial ecoregion
-- Current land cover (5 constraints): Shrub land, Herbaceous vegetation, Agricultural land, Urban / built up, Bare / sparse vegetation, Snow and ice, Herbaceous wetland, Moss and lichen
-- Forest change (3 constraints): deforestation rate, climate risk, natural regeneration variability
-- Socio-economic constraints (6 constraints): protected areas, population density, declining population, property rights protection, accessibility to cities
+- **Biophysical**, including elevation, slope, annual rainfall, baseline water stress and terrestrial ecoregion;
+- **Current land cover**, including shrubland, herbaceous vegetation, agricultural land, urban/built up, bare/sparse vegetation, snow and ice, herbaceous wetland, and moss and lichen;
+- **Forest change**, including deforestation rate, climate risk and natural regeneration variability; and
+- **Socioeconomic**, including protected areas, population density, declining population, property rights protection, and accessibility to cities.
-**se.plan** enables the user to adjust the values that will be masked from the analysis for most of these constraints. Some of the constraints are binary variables, with a value of 1 if a site has the characteristic associated with the variable and 0 if it does not. For these constraints, users can choose if they want to keep zeros or ones.
-
-Please see :ref:`seplan-appendix-f` for more detail on the interpretation and generation of the data layers for the constraints.
+**se.plan** enables the user to adjust the values that will be masked from the analysis for most of these constraints. Some of the constraints are binary variables, with a value of 1 if a site has the characteristic associated with the variable and 0 if it does not. For these constraints, users can choose if they want to keep zeros or ones. (See :ref:`seplan-appendix-f` for more information on the interpretation and generation of the data layers for the constraints.)
Customization
^^^^^^^^^^^^^
-Every Constraints, Costs and Indicators are based on layers provided within the tools. These layer may not be covering the AOI selected by the user or provide less accurate/updated data than the National datasets available. To allow user to improve the quality of the analysis **se.plan** provides the possiblity of replacing these datasets by any layer available with Google Earth Engine.
+Constraints, costs and indicators are based on layers provided within the tools. These layers may not cover the AOI selected by the user, or may provide less accurate/up-to-date data than national datasets available. To allow users to improve the quality of the analysis, **se.plan** provides the possiblity of replacing these datasets by any layer available with GEE.
-Please see :ref:`seplan-usage` section for more details on the customization process.
+(See the :ref:`seplan-usage` section for more information on the customization process.)
Output
^^^^^^
**se.plan** provides two outputs:
-- A map of the Restoration suitability index scaled from 1 (low suitability) to 5 (high suitability). This map, generated within the Google Earth Engine API can be displayed in the app but also exported as a GEE asset or a :code:`.tif` file in your SEPAL folders.
+- A map of the restoration suitability index scaled from 1 (low suitability) to 5 (high suitability). This map, generated within the GEE API can be displayed in the app but also exported as a GEE asset or a :code:`.tif` file in your SEPAL folders.
.. thumbnail:: https://raw.githubusercontent.com/12rambau/restoration_planning_module/master/doc/img/restoration_map.png
- :title: The map produced by Seplan showing which areas are best suited for restoration according to the select costs, benefits and constraints
+ :title: The map produced by se.plan showing which areas are best suited for restoration according to the selected costs, benefits and constraints.
:group: se.plan
-- A dashboard gathering informations on the AOI and sub-AOIs defined by the users. The suitability index is thus presented as surfaces in Mha but **se.plan** also displays the mean values of the benefits and the sum of all the used constraints and cost over the AOIs.
+- A dashboard gathering information on the AOI and sub-AOIs defined by the user. The suitability index is thus presented as surfaces in mega hectares (Mha), but **se.plan** also displays the mean values of the benefits and the sum of all the used constraints and cost over the AOIs.
.. thumbnail:: https://raw.githubusercontent.com/12rambau/restoration_planning_module/master/doc/img/dashboard_region.png
- :title: The dashboard produced by Seplan showing which areas are best suited for restoration according to the select costs, benefits and constraints
+ :title: The dashboard produced by se.plan showing which areas are best suited for restoration, according to the select costs, benefits and constraints.
:group: se.plan
.. _seplan-usage:
@@ -119,36 +138,37 @@ In this section, we will exaustively describe how to use the **se.plan** applica
Open the app
^^^^^^^^^^^^
-To access the application, please connect to your SEPAL account following this link: https://sepal.io/.
+To access the application, open your SEPAL account by going to https://sepal.io.
-Then click on the purple wrench on the right side of your screen to access the dashboard of application (https://sepal.io/app-launch-pad). On this page all the available applications of SEPAL are displayed.
+Then, select the purple wrench on the right side of your screen to access the **Apps** dashboard (https://sepal.io/app-launch-pad). All available SEPAL applications are displayed on this page.
.. image:: https://raw.githubusercontent.com/12rambau/restoration_planning_module/master/doc/img/app_dashboard.png
- :alt: app dashboard
+ :alt: Apps dashboard
-In the app dashboard, type "se.plan" in the search bar. The list of application should be reduce to one single application.
+In the **Apps** dashboard, enter **se.plan** in the search bar. The list of applications should be reduced to one.
.. image:: https://raw.githubusercontent.com/12rambau/restoration_planning_module/master/doc/img/app_dashboard_filter.png
:alt: app dashboard
-
-Click on it and wait until the loading is finished. The application will display the about page.
+Select the **se.plan** app and wait until the loading is finished. The application will display the **About** page.
.. note::
- You might need to manually start an instance that is more powerful than the default t1 instance. Refer to `Module <../module/index.html>`__` section to see how to start instances.
+ You might need to manually start an instance that is more powerful than the default **t1** instance (see the `Module <../module/index.html>`__` section to learn how to start instances).
.. image:: https://raw.githubusercontent.com/12rambau/restoration_planning_module/master/doc/img/landing_page.png
:alt: landing_page
-Use the left side drawers to navigate through the application panels.
+Use the drawers on the left to navigate through the application panes.
The next sections will guide you through each step of the **se.plan** process.
Select AOI
^^^^^^^^^^
-The *restoration suitability index* (hereinafter referred to as *index*) will be calculated based on the user inputs. The first mandatory input is the Area Of Interest (AOI). In this step you’ll have the possibility to choose from a predefined list of administrative layers or use your own datasets, the available options are:
+The **Restoration suitability index** (referred to as **Index**) will be calculated based on user inputs.
+
+The first mandatory input is the AOI. In this step, you’ll have the possibility to choose from a predefined list of administrative layers or use your own datasets. Available options include:
**Predefined layers**
@@ -160,20 +180,20 @@ The *restoration suitability index* (hereinafter referred to as *index*) will be
- Vector file
- Drawn shapes on map
-- Google Earth Engine Asset
+- GEE asset
-After selecting the desired area, click over the :code:`Select these inputs` button and the map shows up your selection. Once you see the confirmation green message, click on the “Questionnaire” panel to move to the next step.
+After selecting the desired area, select the :code:`Select these inputs` button; the map will display your selection. Once you see the green confirmation message, select the **Questionnaire** pane to move to the next step.
.. note::
- You can only select one area of interest. In some cases, depending on the input data you could run out of resources in GEE.
+ You can only select one AOI. In some cases, depending on the input data, you could run out of resources in GEE.
.. image:: https://raw.githubusercontent.com/12rambau/restoration_planning_module/master/doc/img/aoi_selection.png
:alt: AOI selection
-.. warning::
+.. attention::
- As described in the first section of this manual, the layers provided in this application are covering the 139 countries defined as LMIC by the *World Bank*. If the selected AOI is out of these boundaries, then the provided layers cannot be used to compute the *index*. A warning message will remind the user that every used layer will thus need to be replaced by a custom one that will conver the missing area.
+ As described in the first section of this article, the layers provided in this application cover the 139 countries defined as LMICs by the World Bank. If the selected AOI is out of these boundaries, the provided layers cannot be used to compute the **Index**. A warning message will remind the user that every used layer will thus need to be replaced by a custom one that will cover the missing area.
.. image:: https://raw.githubusercontent.com/12rambau/restoration_planning_module/master/doc/img/out_of_lmic_selection.png
:alt: Out of LMIC AOI
@@ -181,23 +201,26 @@ After selecting the desired area, click over the :code:`Select these inputs` but
Questionnaire
^^^^^^^^^^^^^
-The questionnaire is split in 2 steps, the constraints that will narrow the spatial extend of the computation and the benefits that will allow the user to customize the priorities of its restoration analysis.
+The questionnaire is divided into two steps:
+
+- the constraints that will narrow the spatial extent of the computation; and
+- the benefits that will allow the user to customize the priorities of the restoration analysis.
Select constraints
******************
-.. warning::
+.. attention::
- This panel cannot be used prior to select an AOI
+ This pane cannot be used prior to selecting an AOI.
-**se.plan** allows users to set constraints limiting restoration to only those sites they view as suitable, in view of ecological and socioeconomic risks. It groups the constraints into four categories:
+**se.plan** allows users to set constraints limiting restoration to only those sites they view as suitable, specifically in light of ecological and socioeconomic risks. The tool groups the constraints into four categories:
-- Biophysical (5 constraints): elevation, slope, annual rainfall, baseline water stress, terrestrial ecoregion
-- Current land cover (8 constraints): Shrubs, Herbaceous vegetation, Cultivated and managed vegetation/agriculture, Urban / built up, Bare / sparse vegetation, Snow and ice, Herbaceous wetland, Moss and lichen
-- Forest change (3 constraints): deforestation rate, climate risk, natural regeneration variability
-- Socio-economic constraints (6 constraints): protected areas, population density, declining population, property rights protection, accessibility to cities
+- **Biophysical constraints**, including elevation, slope, annual rainfall, baseline water stress and terrestrial ecoregion;
+- **Current land cover**, including shrubs, herbaceous vegetation, cultivated and managed vegetation/agriculture, urban/built up, bare/sparse vegetation, snow and ice, herbaceous wetland, and moss and lichen;
+- **Forest change**: deforestation rate, climate risk and natural regeneration variability; and
+- **Socioeconomic constraints**, including protected areas, population density, declining population, property rights protection and accessibility to cities.
-These categories are displayed to the user in expandable panels. Simply click on it to open its panel and select the appropriate constraint name in the dropdown menu labeled "criteria". The constraints customization will appear underneath.
+These categories are displayed to the user in expandable panes. Select a category to open its pane and choose the appropriate constraint name in the dropdown menu labeled **Criteria**. The customization of contraints will appear underneath.
.. image:: https://raw.githubusercontent.com/12rambau/restoration_planning_module/master/doc/img/constraints.png
:alt: constraints
@@ -206,56 +229,54 @@ Some constraints are numerical or categorical, for which **se.plan** enables the
.. tip::
- The values provided in the slider are computed on the fly over your AOI preventing the user from selecting a filter that would remove all pixels in your Area.
+ The values provided in the slider are computed on the fly over your AOI preventing the selection of a filter that would remove all pixels in your area.
.. image:: https://raw.githubusercontent.com/12rambau/restoration_planning_module/master/doc/img/slider.png
:alt: binary
-Other constraints are binary variables, with a value of 1 if a site has the characteristic associated with the variable and 0 if it does not. On the application it displays as a switch. For these constraints, users can choose if they want to keep zeros (switch off) or ones (switch on)..
+Other constraints are binary variables (a value of 1 if a site has the characteristic associated with the variable, or a value of 0 if it does not), which can be set using a switch. For these constraints, users can choose if they want to keep 0s (switch off) or 1s (switch on).
.. image:: https://raw.githubusercontent.com/12rambau/restoration_planning_module/master/doc/img/binaries.png
:alt: binary
-Once the selection is finished, the selected constraints will be displayed as small chips in the expandable panel title, allowing the user to see all the selected constraints at a glance.
-
+Once the selection is finished, the chosen constraints will be displayed as small chips in the expandable pane title, allowing the user to see all the selected constraints at a glance.
.. image:: https://raw.githubusercontent.com/12rambau/restoration_planning_module/master/doc/img/chips.png
- :alt: constraints chips
+ :alt: Constraints chips
-Every selected constraints is corresponding to a layer provided by **se.plan** listed in :ref:`seplan-appendix-f`. These layers can be customized in this panel to use national data or to provide information on areas that are not covered by the tool default layers. You do not need to add constraints if there isn’t any. In this case, default values will be used and you can simply proceed to the next steps.
+Every selected constraint corresponds to a layer provided by **se.plan** (listed in the section, :ref:`seplan-appendix-f`). These layers can be customized in this pane to use national data or to provide information on areas that are not covered by the tool's default layers. You do not need to add constraints if there aren't any. In this case, default values will be used and you can simply proceed to the next steps.
.. note::
- To use a customized dataset, it need to be uploaded as a :code:`ee.Image` in Google Earth Engine.
+ To use a customized dataset, it needs to be uploaded as an :code:`ee.Image` in GEE.
-Click on the pencil on the left side of the layer name and a popup will rise on the screen. It includes multiple information:
+Select the pencil on the left side of the layer name and a pop-up window will appear, which provides:
-- The layer name as it can be found in GEE
-- The unit of the provided layer
-- A map displaying the layer over the AOI using a linear viridis color scale (the legend is in the bottom left corner)
+- the layer name as it can be found in GEE;
+- the unit of the provided layer; and
+- a map displaying the layer over the AOI using a linear viridis color scale (the legend is in the lower-left corner).
-The user can change the layer to any other image from GEE. The map will update automatically to display this new layer and change the legend. If the provided layer uses another unit please change it. This unit will be used in the final report of **se.plan**.
+The user can change the layer to any other image from GEE. The map will update automatically to display this new layer and change the legend. If the provided layer uses another unit, please change it. This unit will be used in **se.plan's** final report.
-.. warning::
+.. attention::
- The user needs to have access to the provided custom layer to use it. if the asset cannot be accessed the application will fallback to the default one.
+ The user needs to have access to the provided custom layer to use it. If the asset cannot be accessed, the application will revert to the default.
-Once the modifications are finished click on :code:`save` to apply the changes to the layer. If the constraint is non binary, the slider values will be updated to the customized dataset.
+Once the modifications are finished, select :code:`save` to apply the changes to the layer. If the constraint is non-binary, the slider values will be updated to the customized dataset.
-.. warning::
+.. attention::
- Don't forget to change the slider values after a layer customization. If your layer uses a different unit, all the pixels might be included in your filtering parameters.
+ Don't forget to change the slider values after a layer customization. If your layer uses a different unit, all pixels might be included in your filtering parameters.
.. image:: https://raw.githubusercontent.com/12rambau/restoration_planning_module/master/doc/img/custom_constraints.gif
- :alt: constraints customization
-
+ :alt: Constraints customization
-Select Indicators
+Select indicators
*****************
-Users rate the relative importance of benefits from their standpoint (or the standpoint of stakeholders they represent), and **se.plan** then calculates an *index* that indicates each grid cell’s relative restoration value aggregated across all four benefit categories. To rate each indicator, the user simply ticks the corresponding checkbox.
+Users rate the relative importance of benefits from their standpoint (or the standpoint of stakeholders they represent); then, **se.plan** calculates an **Index** that indicates each grid cell’s relative restoration value aggregated across all four benefit categories. To rate each indicator, the user simply ticks the corresponding checkbox.
-.. warning::
+.. attention::
This step is mandatory if you would like to perform an analysis. If every indicator is set to low (0), then the final output will be 0 everywhere.
@@ -264,7 +285,7 @@ Users rate the relative importance of benefits from their standpoint (or the sta
.. tip::
- Using the pencil icon next to the indicator name, the user can customize the layer used by **se.plan** to compute its *index*. The editing popup panel is the same as the one presented in the previous section.
+ Utilizing the pencil icon next to the indicator name, the user can customize the layer used by **se.plan** to compute its **Index** (the editing pop-up pane is the same as the one presented in the previous section).
.. image:: https://raw.githubusercontent.com/12rambau/restoration_planning_module/master/doc/img/indicator_custom.gif
:alt: indicators custom
@@ -272,7 +293,7 @@ Users rate the relative importance of benefits from their standpoint (or the sta
Select costs
************
-User can customize the layers that will be used as **costs** in the weighted sum approach. To change it the user will go to the third tab of the questionnaire panel ("COSTS") and click on the :icon:`fa-solid fa-pencil` to open the modification dialog interface. The editing popup panel is the same as the one presented in the previous section.
+Users can customize the layers that will be used as **Costs** in the weighted sum approach by going to the third tab of the questionnaire pane (**Costs**) and selecting the :icon:`fa-solid fa-pencil` to open the modification dialog interface (the editing pop-up pane is the same as the one presented in the previous section).
.. image:: https://raw.githubusercontent.com/12rambau/restoration_planning_module/master/doc/img/costs.png
:alt: indicators
@@ -280,146 +301,148 @@ User can customize the layers that will be used as **costs** in the weighted sum
Recipe
^^^^^^
-Next go to the Recipe panel. Recipe is the base information use by **se.plan** to compute the *restoration suitability index*. It's a :code:`.json` serialized version of all the inputs the user provided in the previous steps. It can be shared and reused by other users. You need to validate your recipe before proceed to the results. By clicking the “Save your recipe” button, all your customization in previous steps are recorded and validated.
+Next, go to the **Recipe** pane.
+
+**Recipe** is the base information used by **se.plan** to compute the **Restoration suitability index**, which is a :code:`.json` serialized version of all user-provided inputs in the previous steps that can be shared and reused by other users.
+
+You need to validate your recipe before proceeding to the results. By selecting the **Save your recipe** button, customization completed in previous steps is recorded and validated.
Validate recipe
***************
-.. warning::
+.. attention::
- The AOI and Questionnaire steps need to be completed to validate the recipe.
+ The **AOI** and **Questionnaire** steps need to be completed to validate the recipe.
-First the user should provide a name for its recipe. By default **se.plan** will use the current date but this can be specified to anything else.
+First, the user should provide a name for the recipe. By default, **se.plan** uses the current date; however, this can be changed.
.. note::
- If unauthorized folder characters (:code:`"`, :code:`\`, :code:`/`, :code:` `) are used they will be automatically replaced by :code:`_`.
+ If unauthorized folder characters (:code:`"`, :code:`\`, :code:`/`, :code:` `) are used, they will be automatically replaced by :code:`_`.
-Once all the required inputs are provided, the user can validate the recipe by clicking on the :guilabel:`validate recipe` button.
+Once all required inputs are provided, the user can validate the recipe by selecting the :guilabel:`validate recipe` button.
-A :code:`.json` file will be created in the :code:`module_result/restoration_planning_module/` directory of your SEPAL workspace and a sum-up of your inputs wil be displayed in expandable panels.
+A :code:`.json` file will be created in the :code:`module_result/restoration_planning_module/` directory of your SEPAL workspace and a summary of your inputs wil be displayed in expandable panes.
.. image:: https://raw.githubusercontent.com/12rambau/restoration_planning_module/master/doc/img/valid_recipe.png
:alt: valid recipe
-In the benefits section of the expandable panels, the user will find the list of indicators sets in the questionnaire with the selected wheights. If they are not matching its restoration priorities, they can still be modified in the questionnaire section.
+In the **Benefits** section of the expandable panes, the user will find the list of indicators set in the **Questionnaire** with the selected weights. If they do not match restoration priorities, they can still be modified in the **Questionnaire** section.
.. note::
- Don't forget to validate again the recipe every time a change is made in the prior sections (AOI selector and/or Quetionnaire).
+ Don't forget to validate the recipe every time a change is made in the prior sections (**AOI selector** and/or **Quetionnaire**).
.. image:: https://raw.githubusercontent.com/12rambau/restoration_planning_module/master/doc/img/indicators_recipe.png
- :alt: indicators recipe
+ :alt: Indicators recipe
-In the Constraints section of the expandable panels, the user will find the complete list of available constraints in the tool. The activated one will be displayed in blue. The red one will be ignored in the computation of the *restoration suitability index*.
+In the **Constraints** section of the expandable panes, the user will find the complete list of available constraints in the tool. The activated constraint will be displayed in blue; the constraint in red will be ignored in the computation of the **Restoration suitability index**.
.. image:: https://raw.githubusercontent.com/12rambau/restoration_planning_module/master/doc/img/constraints_recipe.png
- :alt: constraints recipe
-
+ :alt: Constraints recipe
Use existing recipe
*******************
.. tip::
- Loading a recipe can be done without setting any AOI or questionnaire answers.
+ Loading a recipe can be done without setting any **AOI** or **Questionnaire** answers.
-The recipe is a simple :code:`.json` file. it's meant to be shared and reused. To to so simply use the file selector of the recipe panel and select a recipe from your SEPAL workspace folder.
+The recipe is a simple :code:`.json` file that is meant to be shared and reused. To do so, use the file selector of the **Recipe** pane and select a recipe from your **SEPAL workspace** folder.
.. note::
- Only the :code:`.json` files will be available.
- - If you've just uploaded the file, hit the :code:`reload` button to reload the file list of the menu.
+ - If you've just uploaded the file, select the :code:`reload` button to update the file list.
.. tip::
- By default the file selector is pointing where **se.plan** is saving recipes and results. If the user wants to access the rest of its SEPAL workspace, simply click on the :code:`parent` link in the popup menu (on top of the list).
+ By default, the file selector displays the folder where **se.plan** saves recipes and results. If the user wants to access the rest of their **SEPAL workspace**, select the :code:`parent` link in the pop-up menu (on top of the list).
-Once the user will click on :code:`apply the selected recipe`, **se.plan** will reload the AOI specified in the recipe and changed all the questionnaire answers according to the loaded recipe. It's then automatically validated.
+Once the user selects :code:`apply the selected recipe`, **se.plan** will reload the AOI specified in the recipe and change all questionnaire answers according to the loaded recipe. It is then automatically validated.
.. image:: https://raw.githubusercontent.com/12rambau/restoration_planning_module/master/doc/img/load_recipe.gif
- :alt: constraints recipe
+ :alt: Constraints recipe
+Results map
+^^^^^^^^^^^
-Result map
-^^^^^^^^^^
-
-.. warning::
+.. attention::
- the recipe needs to be validated
+ The recipe needs to be validated.
-Once the recipe is validated, the :guilabel:`compute the restoration map` button is released and the *restoration suitability index* can be computed. Click the button to view the results map.
+Once the recipe is validated, the :guilabel:`compute the restoration map` button is released and the **Restoration suitability index** can be computed. Select the button to view the results map.
-The map will be centered on the selected AOI and the value of the *index* will be displayed from 1 to 5 using a color blind friendly color ramp, red being "not suitable" and blue "very suitable".
+The map will be centred on the selected AOI and the value of the **Index** will be displayed from 1 to 5 using a color-blind, friendly color ramp (red being "not suitable" and blue "very suitable").
.. image:: https://raw.githubusercontent.com/12rambau/restoration_planning_module/master/doc/img/restoration_map.png
- :alt: restoration map
+ :alt: Restoration map
.. note::
- The map can be downloaded as an asset to GEE or as a :code:`.tif` file. Click on the :icon:`fa-solid fa-cloud-arrow-down` button on the top left corner and follow the exportation instructions.
+ The map can be downloaded as an asset to GEE or as a :code:`.tif` file. Select the :icon:`fa-solid fa-cloud-arrow-down` button in the upper-left corner and follow the exportation instructions.
Compute dashboard
^^^^^^^^^^^^^^^^^
-The compute dashboard button is initially deactivated, and will be activated after the results map correctly returned. Click on this button to view the dashboard where results will be displayed (see next section “Restoration dashboard”). The dashboard is a report of all the restoration information gathered by **se.plan** during the computation. It is run from the map and displayed in the "dasboard" page.
+The **Compute dashboard** button is initially deactivated and will be activated after the **Results map** correctly returns. Select this button to view the dashboard where results will be displayed (see the section, Restoration dashboard). The dashboard is a report of all restoration information gathered by **se.plan** during the computation, run from the map and displayed on the **Dasboard** page.
Select sub-AOI
**************
-The Results from **se.plan** are given for the initial AOI. users can also provide sub-AOIs to the tool to provide extra information on smaller areas. The sub-area are not mandatory to compute the dashboard.
+The **Results** from **se.plan** are given for the initial AOI. Users can also provide sub-AOIs to the tool to provide extra information on smaller areas (the sub-areas are not mandatory to compute the dashboard).
.. important::
- Using sub-AOI is the only way to compare results for different zones as the normalization have been performed on the full extend of the initial AOI.
+ Using sub-AOI is the only way to compare results for different zones, as normalization has been performed on the full extent of the initial AOI.
-The sub-AOIs can be selected using a shapefile. The sub-AOIs names will be the one set in the selected property.
+The sub-AOIs can be selected using a shapefile. The names of the sub-AOIs will be the name set in the selected property.
.. image:: https://raw.githubusercontent.com/12rambau/restoration_planning_module/master/doc/img/load_shp.gif
:alt: load shp
-They can also be directly drawn on the map. There are three buttons under the cloud icon where you can choose to draw a polygon, a rectangle or a circle. Click any of them based on your needs. Each time a new geometry is drawned, a popup dialogue will ask the user to name it. This name will be used in the final report. You will need to click the compute dashboard button again to include all the sub-AOIs in the report.
+The sub-AOIs can also be drawn directly on the map. There are three buttons under the cloud icon where you can choose to draw a polygon, rectangle or circle. Select any of them based on your needs. Each time a new geometry is drawn, a pop-up dialogue will ask the user to name it. This name will be used in the final report. You will need to select the **Compute dashboard** button again to include all sub-AOIs in the report.
.. note::
- The user still have the possiblity to remove some geometry by clicking on the :icon:`fa-solid fa-trash-can` button on the map but editing is not possible.
+ The user can still remove some geometry by selecting the :icon:`fa-solid fa-trash-can` button on the map; however, editing is not possible.
-.. danger::
+.. attention::
- Once the dashboard have been computed, sub-AOIs will be validated (a different color for each one of them) and it will be impossible to remove them. New geometries can still be added.
+ Once the dashboard has been computed, sub-AOIs will be validated (a different color for each); it will be impossible to remove them. New geometries can still be added.
.. image:: https://raw.githubusercontent.com/12rambau/restoration_planning_module/master/doc/img/custom_sub_aoi.gif
- :alt: custom sub aoi
+ :alt: Custom sub-AOI
Restoration dashboard
*********************
-After clicking on :code:`compute dashboard` button, The report generated from the previous step is displayed in this panel.
+After selecting the :code:`compute dashboard` button, the report generated from the previous step is displayed in the pane.
-.. warning::
+.. attention::
- This action can take time as GEE needs to export and reduce information on the full extend of the user's initial AOI. Wait until the button stop spinning before changing page.
+ This action can take time, as GEE needs to export and reduce information on the full extent of the user's initial AOI. Wait until the button stops spinning before changing pages.
-Th dasboard has 2 sections:
+The dasboard has two sections:
-#. Summary of restoration suitability by region
-#. Area of interest - summary by subthemes
+#. **Summary of restoration suitability by region**
+#. **AOI - summary by sub-theme**
-In the first one, the *restoration suitability index* is given as proportion of the AOI and the sub-AOIs. ISO3 codes rather than country names are used. Click on the details panel to get the surfaces of each restoration value in *MHa*.
+In the first section, the **Restoration suitability index** is given as proportion of the AOI and the sub-AOIs (note: ISO3 codes are used rather than country names. Select the **Details** pane to get the surfaces of each restoration value in MHa.
-The names use for AOIs are the one selected in the map.
+The names used for AOIs are the names selected on the map.
.. image:: https://raw.githubusercontent.com/12rambau/restoration_planning_module/master/doc/img/dashboard_region.png
- :alt: regional dashboard
+ :alt: Regional dashboard
-In the second section, the summary is given by subtheme:
+In the second section, the summary is given by sub-theme:
**Benefits**
-The mean value of each benefits is displayed in a bar chart. These charts use the unit corresponding to each layer and display the value for each sub-AOI. Value will be using the SI prefixes if the value is not readable in the original unit. The main AOI is first displayed in gold and the sub-AOIs are displayed using the color attributed when the dashboard was computed (i.e. the same as the one used on the map).
+The mean value of each benefit is displayed in a bar chart. These charts use the unit corresponding to each layer and display the value for each sub-AOI. Value will be using prefixes from the International System of Units (SI) if the value is not readable in the original unit. The main AOI is first displayed in gold and the sub-AOIs are displayed in the color attributed when the dashboard was computed (i.e. the same as the one used on the map).
.. image:: https://raw.githubusercontent.com/12rambau/restoration_planning_module/master/doc/img/dashboard_benefits.png
- :alt: dashboard benefits
+ :alt: Dashboard benefits
**Costs**
@@ -427,36 +450,38 @@ The sum of each cost over the AOI is displayed in bar charts in the same fashion
.. tip::
- If the surface difference between the main AOI and sub-AOIs is important as in this example, the summed value will also be vastly different.
+ If the surface difference between the main AOI and sub-AOIs is important, as in this example, the total value will also be vastly different.
.. image:: https://raw.githubusercontent.com/12rambau/restoration_planning_module/master/doc/img/dashboard_costs.png
- :alt: dashboard costs
+ :alt: Dashboard costs
**Constraints**
-The constraints are displayed in percentages. Each value represents the percentage of surface affected by the filter applied by this constraint over the AOI. each color represent an AOI: gold for the main AOI and the automatically attributed colors of the sub-AOIs.
+The constraints are displayed in percentages. Each value represents the percentage of surface affected by the filter applied by this constraint over the AOI. Each color represents an AOI (gold for the main AOI and the automatically attributed colors of the sub-AOIs).
.. image:: https://raw.githubusercontent.com/12rambau/restoration_planning_module/master/doc/img/dashboard_constraints.png
- :alt: dashboard costs
+ :alt: Dashboard costs
.. note::
- THe dashboard is also exported in .csv format to be easily interpreted in any spreadsheet software. It is stored at the same place as the recipe in :code:`module_results/se.plan/`.
+ The dashboard is also exported in .csv format to be easily interpreted in any spreadsheet software. It is stored in the same location as the recipe in :code:`module_results/se.plan/`.
.. _seplan-appendix-a:
Primary data sources
--------------------
-The **se.plan** team obtained data for the default spatial layers in the tool from various sources. It determined potential tree cover using data from:
+The **se.plan** team obtained data for the default spatial layers in the tool from the following sources.
- J.F. Bastin, Y. Finegold, C. Garcia, et al., 2019, The global tree restoration potential, Science 365(6448), pp. 76–79, doi:`10.1126/science.aax084 `_
+For determining potential tree cover, data was used from:
-It determined current tree cover using data from:
+ Bastin, J.F., Finegold, Y., Garcia, C. *et al.* 2019. The global tree restoration potential. *Science*, 365(6448), pp. 76–79. DOI:`10.1126/science.aax084 `_
- \M. Buchhorn, M. Lesiv, N.E. Tsendbazar, M. Herold, L. Bertels, B. Smets, 2020, Copernicus Global Land Cover Layers—Collection 2. Remote Sensing, 12 Volume 108, 1044. doi:`10.3390/rs12061044 `_
+For determining current tree cover, data was used from:
-It drew data for remaining spatial layers primarily from the following sources. For additional detail, see :ref:`seplan-appendix-c` (benefits), :ref:`seplan-appendix-e` (costs), and :ref:`seplan-appendix-f` (constraints).
+ Buchhorn, M., Lesiv, M., Tsendbazar, N.E., Herold, M., Bertels, L. and Smets, B. 2020. Copernicus Global Land Cover Layers—Collection 2. *Remote Sensing*, 12(108): 1044. doi:`10.3390/rs12061044 `_
+
+The team took data for the remaining spatial layers primarily from the sources presented in the following tables (for more information, see :ref:`seplan-appendix-c` [benefits], :ref:`seplan-appendix-e` [costs], and :ref:`seplan-appendix-f` [constraints]).
Costs
^^^^^
@@ -465,18 +490,17 @@ Costs
:header-rows: 1
Spatial layer, Data sources
- Land opportunity cost, "International Food Policy Research Institute, 2019, Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 2.0, https://doi.org/10.7910/DVN/PRFF8V, Harvard Dataverse, V4"
- , "UN FAO, 2020, FAOSTAT: Crops, http://www.fao.org/faostat/en/#data/QC"
- , "UN FAO, 2007, Occurrence of Pasture and Browse (FGGD), https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/913e79a0-7591-11db-b9b2-000d939bc5d8"
- , "ESA, 2017, Land Cover CCI Product User Guide, Version2, maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf"
- , "UN FAO, 2018, Gridded Livestock of the World – Latest – 2010 (GLW 3), https://dataverse.harvard.edu/dataverse/glw_3, Harvard Dataverse, V3"
- , "UN FAO, 2020, FAOSTAT: Livestock Primary, http://www.fao.org/faostat/en/#data/QL"
- , "UN FAO, 2020, RuLIS - Rural Livelihoods Information System, http://www.fao.org/in-action/rural-livelihoods-dataset-rulis/en/"
- , "World Bank, 2020, World Development Indicators, https://databank.worldbank.org/source/world-development-indicators"
- , "CIESIN (Center for International Earth Science Information Network), 2018, Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11, NASA Socioeconomic Data and Applications Center (SEDAC), https://doi.org/10.7927/H49C6VHW"
- , "\M. Kummu, M. Taka, & J. Guillaume, 2018, Gridded global datasets for Gross Domestic Product and Human Development Index over 1990–2015, Scientific Data 5, 180004, https://doi.org/10.1038/sdata.2018.4"
- Establishment cost, "World Bank, various years, Projects & Operations [project appraisal documents and implementation completion reports for selected projects], https://projects.worldbank.org/en/projects-operations/projects-home"
-
+ Land opportunity cost, "International Food Policy Research Institute. 2019. Global Spatially-Disaggregated Crop Production Statistics Data for 2010 Version 2.0. Harvard Dataverse, V4. https://doi.org/10.7910/DVN/PRFF8V"
+ , "FAO (Food and Agriculture Organization of the United Nations). 2020. FAOSTAT: Crops. http://www.fao.org/faostat/en/#data/QC"
+ , "FAO. 2007. Occurrence of Pasture and Browse (FGGD). https://data.apps.fao.org/map/catalog/srv/eng/catalog.search#/metadata/913e79a0-7591-11db-b9b2-000d939bc5d8"
+ , "ESA (European Space Agency). 2017. Land Cover CCI Product User Guide, Version 2. maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf"
+ , "FAO. 2018. Gridded Livestock of the World – Latest – 2010 (GLW 3). https://dataverse.harvard.edu/dataverse/glw_3, Harvard Dataverse, V3"
+ , "FAO. 2020. FAOSTAT: Livestock Primary. http://www.fao.org/faostat/en/#data/QL"
+ , "FAO. 2020. RuLIS - Rural Livelihoods Information System. http://www.fao.org/in-action/rural-livelihoods-dataset-rulis/en"
+ , "World Bank. 2020. World Development Indicators. https://databank.worldbank.org/source/world-development-indicators"
+ , "CIESIN (Center for International Earth Science Information Network). 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H49C6VHW"
+ , "Kummu, M., Taka, M. and Guillaume, J. 2018. Gridded global datasets for Gross Domestic Product and Human Development Index over 1990–2015. *Scientific Data*, 5: 180004. https://doi.org/10.1038/sdata.2018.4"
+ Establishment cost, "World Bank. n.d. Projects & Operations [project appraisal documents and implementation completion reports for selected projects]. https://projects.worldbank.org/en/projects-operations/projects-home"
Benefits
^^^^^^^^
@@ -484,67 +508,67 @@ Benefits
.. csv-table::
:header-rows: 1
- Spatial layer, subtheme, Data sources
- Biodiversity intactness index, Biodiversity conservation, "\T. Newbold, L. Hudson, A. Arnell, et al., 2016, Dataset: Global map of the Biodiversity Intactness Index, from Newbold et al., 2016, Science, Natural History Museum Data Portal (data.nhm.ac.uk), https://doi.org/10.5519/0009936"
- Endangered species, Biodiversity conservation, "Layer obtained from World Bank, which processed species range maps from: (i) IUCN, The IUCN Red List of Threatened Species, https://www.iucnredlist.org; and (ii) BirdLife International, Data Zone, http://datazone.birdlife.org/species/requestdis"
- Unrealized biomass potential, Carbon sequestration, "W.S. Walker, S.R. Gorelik, S.C. Cook-Patton et al., 2022, The global potential for increased storage of carbon on land, Proceedings of the National Academy of Sciences, 119(23), p.e2111312119, https://doi.org/10.1073/pnas.2111312119."
- Forest employment, Local livelihoods, "Downscaled estimates generated using national data from: International Labour Organization, 2020, Employment by sex and economic activity - ISIC level 2 (thousands) | Annual, ILOSTAT database, https://ilostat.ilo.org/data"
- Woodfuel harvest, Local livelihoods, "Downscaled estimates generated using national data from: UN FAO, 2020, Forestry Production and Trade, FAOSTAT, http://www.fao.org/faostat/en/#data/FO"
- Plantation growth rate, Wood production, "\F. Albanito, T. Beringer, R. Corstanje, et al., 2016, Carbon implications of converting cropland to bioenergy crops or forest for climate mitigation: a global assessment, GCB Bioenergy 8, pp. 81–95, https://doi.org/10.1111/gcbb.12242"
+ Spatial layer, Sub-theme, Data sources
+ Biodiversity intactness index, Biodiversity conservation, "Newbold, T., Hudson, L., Arnell, A. *et al.* 2016. Dataset: Global map of the Biodiversity Intactness Index. In: Newbold et al. 2016. Science: Natural History Museum Data Portal (data.nhm.ac.uk). https://doi.org/10.5519/0009936"
+ Endangered species, Biodiversity conservation, "Layer obtained from World Bank, which processed species range maps from: (i) IUCN. The IUCN Red List of Threatened Species. https://www.iucnredlist.org; and (ii) BirdLife International. Data Zone. http://datazone.birdlife.org/species/requestdis"
+ Unrealized biomass potential, Carbon sequestration, "Walker, W.S., Gorelik, S.R., Cook-Patton, S.C. *et al.* 2022. The global potential for increased storage of carbon on land. *Proceedings of the National Academy of Sciences*, 119(23): e2111312119. https://doi.org/10.1073/pnas.2111312119"
+ Forest employment, Local livelihoods, "Downscaled estimates generated using national data from: International Labour Organization. 2020. Employment by sex and economic activity - ISIC level 2 (thousands). Annual, ILOSTAT database. https://ilostat.ilo.org/data"
+ Woodfuel harvest, Local livelihoods, "Downscaled estimates generated using national data from: FAO. 2020. Forestry Production and Trade. In: *FAOSTAT*. http://www.fao.org/faostat/en/#data/FO"
+ Plantation growth rate, Wood production, "Albanito, F., Beringer, T., Corstanje, R. *et al.* 2016. Carbon implications of converting cropland to bioenergy crops or forest for climate mitigation: a global assessment. *GCB Bioenergy*, 8: pp. 81–95, https://doi.org/10.1111/gcbb.12242"
Constraints
^^^^^^^^^^^
-biophysical
+Biophysical
***********
.. csv-table::
:header-rows: 1
Spatial layer, Data sources
- Annual rainfall, "Muñoz Sabater, J., (2019): ERA5-Land monthly averaged data from 1981 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://doi.org/10.24381/cds.68d2bb3"
- Baseline water stress, "World Resources Institute, 2021, Aqueduct Global Maps 3.0 Data, https://www.wri.org/data/aqueduct-global-maps-30-data"
- Elevation, "T.G. Farr, P.A. Rosen, E. Caro, et al., 2007, The shuttle radar topography mission: Reviews of Geophysics, v. 45, no. 2, RG2004, at https://doi.org/10.1029/2005RG000183."
- Slope, "T.G. Farr, P.A. Rosen, E. Caro, et al., 2007, The shuttle radar topography mission: Reviews of Geophysics, v. 45, no. 2, RG2004, at https://doi.org/10.1029/2005RG000183."
- Terrestrial ecoregion, "UN FAO, 2012 Global ecological zones for fao forest reporting: 2010 Update, http://www.fao.org/3/ap861e/ap861e.pdf"
+ Annual rainfall, "Muñoz Sabater, J. 2019. ERA5-Land monthly averaged data from 1981 to present. *Copernicus Climate Change Service (C3S) Climate Data Store (CDS)*. https://doi.org/10.24381/cds.68d2bb3"
+ Baseline water stress, "World Resources Institute. 2021. Aqueduct Global Maps 3.0 Data. https://www.wri.org/data/aqueduct-global-maps-30-data"
+ Elevation, "Farr, T.G., Rosen, P.A., Caro, E. *et al.* 2007. The shuttle radar topography mission. *Reviews of Geophysics*, 45(2): RG2004. https://doi.org/10.1029/2005RG000183"
+ Slope, "Farr, T.G., Rosen, P.A., Caro, E. *et al.* 2007. The shuttle radar topography mission. *Reviews of Geophysics*, 45(2): RG2004. https://doi.org/10.1029/2005RG000183"
+ Terrestrial ecoregion, "FAO. 2012. Global ecological zones for FAO forest reporting: 2010 Update. http://www.fao.org/3/ap861e/ap861e.pdf"
-forest change
+Forest change
*************
.. csv-table::
:header-rows: 1
Spatial layer, Data sources
- Climate risk, "J.F. Bastin, Y. Finegold, C. Garcia, et al., 2019, The global tree restoration potential, Science 365(6448), pp. 76–79, DOI: 10.1126/science.aax0848; data downloaded from: https://www.research-collection.ethz.ch/handle/20.500.11850/350258"
- Deforestation rate, "ESA, 2017, Land Cover CCI Product User Guide, Version 2, maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf"
- Natural regeneration variability, "Model from R. Crouzeilles, F.S. Barros, P.G. Molin, et al., 2019, A new approach to map landscape variation in forest restoration success in tropical and temperate forest biomes, J Appl Ecol. 56, pp. 2675– 2686, https://doi.org/10.1111/1365-2664.13501, applied to data from: ESA, 2017, Land Cover CCI Product User Guide, Version 2, maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf"
+ Climate risk, "Bastin, J.F., Finegold, Y., Garcia, C. *et al.* 2019. The global tree restoration potential. *Science*, 365(6448): pp. 76–79. DOI: 10.1126/science.aax0848; data downloaded from: https://www.research-collection.ethz.ch/handle/20.500.11850/350258"
+ Deforestation rate, "ESA. 2017. Land Cover CCI Product User Guide, Version 2. maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf"
+ Natural regeneration variability, "Model from Crouzeilles, R., Barros, F.S., Molin, P.G. *et al.* 2019. A new approach to map landscape variation in forest restoration success in tropical and temperate forest biomes. *Journal of Applied Ecology*, 56: pp. 2675–2686. https://doi.org/10.1111/1365-2664.13501; applied to data from: ESA. 2017. Land Cover CCI Product User Guide, Version 2. maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf"
-socio-economic
+Socioeconomic
**************
.. csv-table::
:header-rows: 1
Spatial layer, Data sources
- Accessibility to cities, "D.J. Weiss, A. Nelson, H.S. Gibson, et al., 2018, A global map of travel time to cities to assess inequalities in accessibility in 2015, Nature, doi:10.1038/nature25181; data downloaded from: https://malariaatlas.org/research-project/accessibility-to-cities/"
- Country risk premium, "\A. Damodaran, 2020, Damodaran Online, http://pages.stern.nyu.edu/~adamodar/"
- Current land cover, "ESA, 2017, Land Cover CCI Product User Guide, Version 2, maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf"
- Declining population, "CIESIN (Center for International Earth Science Information Network), 2018, Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11, NASA Socioeconomic Data and Applications Center (SEDAC), https://doi.org/10.7927/H49C6VHW"
- Governance index, "World Bank, 2020, Worldwide Governance Indicators, https://info.worldbank.org/governance/wgi/"
- Land designated for or owned by IP and LC, "Rights and Resources Initiative, 2015, Who Owns the World’s Land? A global baseline of formally recognized indigenous and community land rights, Washington, DC"
- Net imports of forest products, "UN FAO, 2020, Forestry Production and Trade, FAOSTAT, http://www.fao.org/faostat/en/#data/FO"
- Population density, "CIESIN (Center for International Earth Science Information Network), 2018, Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11, NASA Socioeconomic Data and Applications Center (SEDAC), https://doi.org/10.7927/H49C6VHW"
- Perceived property security, "Prindex, 2020, https://www.prindex.net/"
- Property rights protection, "Downscaled estimates generated using national data from: World Bank, 2020, Worldwide Governance Indicators, https://info.worldbank.org/governance/wgi/"
- Protected area, "IUCN, World Database on Protected Areas, https://www.iucn.org/theme/protected-areas/our-work/world-database-protected-areas"
- Real interest rate, "World Bank, 2020, World Development Indicators, https://databank.worldbank.org/source/world-development-indicators"
+ Accessibility to cities, "Weiss, D.J., Nelson, A., Gibson, H.S. *et al.* 2018. A global map of travel time to cities to assess inequalities in accessibility in 2015. *Nature*. doi:10.1038/nature25181; data downloaded from: https://malariaatlas.org/research-project/accessibility-to-cities"
+ Country risk premium, "Damodaran, A. 2020. Damodaran Online. http://pages.stern.nyu.edu/~adamodar"
+ Current land cover, "ESA. 2017. Land Cover CCI Product User Guide, Version 2. maps.elie.ucl.ac.be/CCI/viewer/download/ESACCI-LC-Ph2-PUGv2_2.0.pdf"
+ Declining population, "CIESIN (Center for International Earth Science Information Network). 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H49C6VHW"
+ Governance index, "World Bank. 2020. Worldwide Governance Indicators. https://info.worldbank.org/governance/wgi/"
+ Land designated for or owned by Indigenous Peoples and local communities (IPLCs), "Rights and Resources Initiative. 2015. Who Owns the World’s Land? A global baseline of formally recognized indigenous and community land rights. Washington, DC."
+ Net imports of forest products, "FAO. 2020. Forestry Production and Trade. In: *FAOSTAT*. http://www.fao.org/faostat/en/#data/FO"
+ Population density, "CIESIN (Center for International Earth Science Information Network). 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. NASA Socioeconomic Data and Applications Center (SEDAC). https://doi.org/10.7927/H49C6VHW"
+ Perceived property security, "Prindex. 2020. https://www.prindex.net"
+ Property rights protection, "Downscaled estimates generated using national data from: World Bank. 2020. Worldwide Governance Indicators. https://info.worldbank.org/governance/wgi"
+ Protected area, "IUCN (International Union for Conservation of Nature). World Database on Protected Areas. https://www.iucn.org/theme/protected-areas/our-work/world-database-protected-areas"
+ Real interest rate, "World Bank. 2020. World Development Indicators. https://databank.worldbank.org/source/world-development-indicators"
.. _seplan-appendix-b:
Countries
---------
-Countries and territories in **se.plan**, by World Bank region.
+Countries and territories in **se.plan** (organized by World Bank region; ISO refers to the International Organization for Standardization; UNI refers to the Italian National Standards Body; UNDP refers to the United Nations Development Programme; FAOSTAT refers to the Food and Agriculture Organization Corporate Statistical Database; GAUL refers to Global Administrative Unit Layers).
East Asia & Pacific
^^^^^^^^^^^^^^^^^^^
@@ -678,7 +702,7 @@ South Asia
Pakistan,the Islamic Republic of Pakistan,PAK,PK,586,PAK,165,188
Sri Lanka,the Democratic Socialist Republic of Sri Lanka,LKA,LK,144,LKA,38,231
-Sub-Saharan Africa
+sub-Saharan Africa
^^^^^^^^^^^^^^^^^^
.. csv-table::
@@ -738,23 +762,25 @@ Sub-Saharan Africa
.. _seplan-appendix-c:
-Benefits data layers
---------------------
+Data layers for benefits
+------------------------
.. note::
- Every data layer presented in the following document can be displayed in Google Earth Engine as an overview of our datasets. Click on the provided link in the description, you'll be redirected to the GEE code editor panel. The selected layer will be displayed over Uganda. To modify the country change the :code:`fao_gaul` variable line 7 by your country number (listed in the Country list section in the rightmost column). If you want to export this layer, please set the value of :code:`to_export` (line 10) and :code:`to_drive` (line 13) according to your need.
+ Every data layer presented in the following document can be displayed in GEE as an overview of our datasets. Select the provided link in the description to be redirected to the **GEE code editor** pane. The selected layer will be displayed over Uganda. To modify the country, change the :code:`fao_gaul` variable Line 7 by your country number (listed in the **Country list** section in the rightmost column). If you want to export this layer, set the value of :code:`to_export` (Line 10) and :code:`to_drive` (Line 13) according to your need.
Hit the :guilabel:`run` button again to relaunch the computation.
- Code used for this display can be found `here `__.
+ Code used for this display can be found `on this page `__.
In its current form, **se.plan** provides information on four categories of potential benefits of forest restoration:
-- Biodiversity conservation
-- Carbon sequestration
-- Local livelihoods
-- Wood production
+- biodiversity conservation
+- carbon sequestration
+- local livelihoods
+- wood production
+
+**se.plan** does not predict the levels of benefits that will occur if forests are restored. Instead, it uses data on benefit-related site characteristics to quantify the potential of a site to provide benefits if it is restored. To clarify this distinction, consider the case of species extinctions. For example, a predictive tool might estimate the number of extinctions avoided if restoration occurs. To do so, it would need to account for restoration scale and interdependencies across sites associated with distances and corridors between restored sites.
-**se.plan** does not predict the levels of benefits that will occur if forests are restored. Instead, it uses data on benefit-related site characteristics to quantify the potential of a site to provide benefits if it is restored. To clarify this distinction, consider the case of species extinctions. A predictive tool might, for example, estimate the number of extinctions avoided if restoration occurs. To do so, it would need to account for restoration scale and interdependencies across sites associated with distances and corridors between restored sites. **se.plan** instead takes a simpler approach: it includes information on the total number of critically endangered and endangered amphibians, reptiles, birds, and mammals at each site. Sites with a larger number of critically endangered and endangered species are ones where the potential number of avoided extinctions is greater. Realizing the benefit of reduced extinctions depends on factors beyond simply restoring an individual site, including the type of forest that is restored (native tree species or introduced tree species, single tree species or multiple tree species, etc.) and the pattern of restoration in the rest of the landscape. Interpreting **se.plan** output in the context of additional, location-specific information available to a user is therefore important.
+**se.plan** takes a simpler approach: the tool includes information on the total number of critically endangered and endangered amphibians, reptiles, birds and mammals at each site. Sites with a larger number of critically endangered and endangered species have a greater potential number of avoided extinctions. Realizing the benefit of reduced extinctions depends on factors beyond simply restoring an individual site, including the type of forest that is restored (native tree species or introduced tree species, single tree species or multiple tree species, etc.) and the pattern of restoration in the rest of the landscape. Therefore, interpreting **se.plan** outputs in the context of additional, location-specific information available to a user is important.
Quantitative measures of potential benefits in **se.plan** should be viewed as averages for a grid cell. Potential benefits could be higher at some locations within a given grid cell and lower at others.
@@ -764,40 +790,40 @@ Quantitative measures of potential benefits in **se.plan** should be viewed as a
* - Variable
- Description
- Source
- * - Endangered species (Biodiversity conservation) in **count**
- - Total number of critically endangered and endangered amphibians, reptiles, birds, and mammals whose ranges overlap a site. Rationale for including in se.plan: sites with a larger number of critically endangered and endangered species are ones where successful forest restoration can potentially contribute to reducing a larger number of extinctions. (`view in gee `__)
- - World Bank, which processed over 25,000 species range maps from: (i) IUCN, The IUCN Red List of Threatened Species, https://www.iucnredlist.org; and (ii) BirdLife International, Data Zone, http://datazone.birdlife.org/species/requestdis. Resolution of World Bank layer: 1 kilometer. More information may be found at https://datacatalog.worldbank.org/dataset/terrestrial-biodiversity-indicators, and data may be downloaded at http://wbg-terre-biodiv.s3.amazonaws.com/listing.html. See also: (i) Dasgupta, Susmita; Wheeler, David. 2016. Minimizing Ecological Damage from Road Improvement in Tropical Forests. Policy Research Working Paper: No. 7826. World Bank, Washington, DC. (ii) Danyo Stephen, Susmita Dasgupta and David Wheeler. 2018. Potential Forest Loss and Biodiversity Risks from Road Improvement in Lao PDR. World Bank Policy Research Working Paper 8569. World Bank, Washington, DC. (iii) Damania Richard, Jason Russ, David Wheeler and Alvaro Federico Barra. 2018. The Road to Growth: Measuring the Tradeoffs between Economic Growth and Ecological Destruction, World Development, Elsevier, vol. 101(C), pp. 351-376.
- * - BII gap (Biodiversity conservation) in **percent**
- - The biodiversity intactness index (BII) describes the average abundance of a large and diverse set of organisms in a given geographical area, relative to the set of originally present species. se.plan subtracts the BII from 100, to measure the gap between full intactness and current intactness. Rationale for including in se.plan: sites with a larger BII gap are ones where successful forest restoration can potentially contribute to reducing a larger gap. (`view in gee `__)
- - \T. Newbold, L. Hudson, A. Arnell, et al., 2016, Dataset: Global map of the Biodiversity Intactness Index, from Newbold et al., 2016, Science, Natural History Museum Data Portal (data.nhm.ac.uk), https://doi.org/10.5519/0009936. Resolution of Newbold et al. layer: 1 km. See also: (i) Scholes, R.J. and Biggs, R., 2005. A biodiversity intactness index. Nature, 434(7029), pp.45-49. (ii) Newbold, T., Hudson, L.N., Arnell, A.P., Contu, S., De Palma, A., Ferrier, S., Hill, S.L., Hoskins, A.J., Lysenko, I., Phillips, H.R. and Burton, V.J., 2016. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science, 353(6296), pp.288-291.
- * - Unrealized biomass potential (Carbon sequestration) in **metric tons C/hectare**
- - Unrealized potential aboveground biomass, belowground biomass, and soil organic carbon combined density (megagrams carbon per hectare) under baseline climate. (see below). (`view in gee `__)
- - W.S. Walker, S.R. Gorelik, S.C. Cook-Patton et al., 2022, The global potential for increased storage of carbon on land, Proceedings of the National Academy of Sciences, 119(23), p.e2111312119, https://doi.org/10.1073/pnas.2111312119. Resolution of Walker et al. layer: 500 m.
- * - Forest employment (Local livelihoods) in **count**
- - Number of forest-related jobs per ha of forest in 2015, summed across three economic activities: forestry, logging, and related service activities; manufacture of wood and of products of wood and cork, except furniture; and manufacture of paper and paper products. Varies by country and, when data are sufficient for downscaling, first-level administrative subdivision (e.g., state or province). Rationale for including in se.plan: a higher level of forest employment implies the existence of attractive business conditions for labor-intensive wood harvesting and processing industries, which tends to make forest restoration more feasible when income for local households is a desired benefit. (`view in gee `__)
- - Developed by se.plan team, by downscaling national data from: International Labour Organization, 2020, Employment by sex and economic activity - ISIC level 2 (thousands) | Annual, ILOSTAT database, https://ilostat.ilo.org/data
- * - Woodfuel harvest (Local livelihoods) in **m3/hectare**
- - Harvest of wood fuel per hectare of forest in 2015. Rationale for including in se.plan: a higher level of wood fuel harvest implies greater demand for wood fuel as an energy source, which tends to make forest restoration more feasible when supply of wood to meet local demands is a desired benefit. (`view in gee `__)
- - Developed by se.plan team, by downscaling national data from: UN FAO, 2020, Forestry Production and Trade, FAOSTAT, http://www.fao.org/faostat/en/#data/FO
- * - Plantation growth rate (Wood production) in **dry metric tons of woody biomass/hectare/year**
- - Potential annual production of woody biomass by fast-growing trees such as eucalypts, poplars, and willows. Rationale for including in se.plan: faster growth of plantation trees tends to make forest restoration more feasible when desired benefits include income for landholders and wood supply to meet local and export demands. (`view in gee `__)
- - \F. Albanito, T. Beringer, R. Corstanje, et al., 2016, Carbon implications of converting cropland to bioenergy crops or forest for climate mitigation: a global assessment, GCB Bioenergy 8, pp. 81–95, https://doi.org/10.1111/gcbb.12242. Resolution of Albanito et al. layer: 55 km.
+ * - Endangered species (biodiversity conservation) in **count**
+ - Total number of critically endangered and endangered amphibians, reptiles, birds and mammals whose ranges overlap a site. Rationale for including in **se.plan**: sites with a larger number of critically endangered and endangered species are ones where successful forest restoration can potentially contribute to reducing a larger number of extinctions (`view in GEE `__).
+ - World Bank, which processed over 25 000 species range maps from: (i) IUCN. The IUCN Red List of Threatened Species. https://www.iucnredlist.org; and (ii) BirdLife International. Data Zone. http://datazone.birdlife.org/species/requestdis. Resolution of World Bank layer: 1 km. More information may be found at https://datacatalog.worldbank.org/dataset/terrestrial-biodiversity-indicators; data may be downloaded at http://wbg-terre-biodiv.s3.amazonaws.com/listing.html. See also: (i) Dasgupta, S. and Wheeler, D. 2016. Minimizing Ecological Damage from Road Improvement in Tropical Forests. Policy Research Working Paper: No. 7826. Washington, DC, World Bank; (ii) Danyo, S., Dasgupta, S. and Wheeler, D. 2018. Potential Forest Loss and Biodiversity Risks from Road Improvement in Lao PDR. World Bank Policy Research Working Paper 8569. Washington, DC, World Bank; (iii) Damania, R., Russ, J., Wheeler, D. and Barra, A.F. 2018. The Road to Growth: Measuring the Tradeoffs between Economic Growth and Ecological Destruction, World Development. Elsevier, 101(C): pp. 351–376.
+ * - Biodiversity Intactness Index (BII) gap (Biodiversity conservation) in **percent**
+ - The BII describes the average abundance of a large and diverse set of organisms in a given geographical area, relative to the set of originally present species. **se.plan** subtracts the BII from 100 to measure the gap between full intactness and current intactness. Rationale for including in **se.plan**: sites with a larger BII gap are ones where successful forest restoration can potentially contribute to reducing a larger gap (`view in GEE `__).
+ - Newbold, T., Hudson, L., Arnell, A. *et al.* 2016. Dataset: Global map of the Biodiversity Intactness Index. In: Newbold *et al.* 2016. Science. Natural History Museum Data Portal (data.nhm.ac.uk). https://doi.org/10.5519/0009936. Resolution of Newbold *et al.* layer: 1 km; see also: (i) Scholes, R.J. and Biggs, R. 2005. A biodiversity intactness index. *Nature*, 434(7029): pp.45-49; (ii) Newbold, T., Hudson, L.N., Arnell, A.P., Contu, S., De Palma, A., Ferrier, S., Hill, S.L., Hoskins, A.J., Lysenko, I., Phillips, H.R. and Burton, V.J. 2016. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. *Science*, 353(6296), pp. 288–291.
+ * - Unrealized biomass potential (carbon sequestration) in **metric tonnes of carbon (C)/hectare**
+ - Unrealized potential above ground biomass, below ground biomass, and soil organic carbon combined density (mega grammes carbon per hectare) under baseline climate (see below) (`view in GEE `__).
+ - Walker, W.S., Gorelik, S.R., Cook-Patton, S.C. *et al.* 2022. The global potential for increased storage of carbon on land. *Proceedings of the National Academy of Sciences*, 119(23): p. e2111312119. https://doi.org/10.1073/pnas.2111312119. Resolution of Walker *et al.* layer: 500 m.
+ * - Forest employment (local livelihoods) in **count**
+ - Number of forest-related jobs per ha of forest in 2015, combined across three economic activities: forestry, logging and related service activities; manufacture of wood and of products of wood and cork, except furniture; and manufacture of paper and paper products. Varies by country and, when data are sufficient for downscaling, first-level administrative subdivision (e.g. state or province). Rationale for including in **se.plan**: a higher level of forest employment implies the existence of attractive business conditions for labor-intensive wood harvesting and processing industries, which tends to make forest restoration more feasible when income for local households is a desired benefit. (`view in GEE `__)
+ - Developed by the **se.plan** team by downscaling national data from: International Labour Organization. 2020. Employment by sex and economic activity - ISIC level 2 (thousands). Annual, ILOSTAT database. https://ilostat.ilo.org/data
+ * - Woodfuel harvest (local livelihoods) in m3/hectare
+ - Harvest of woodfuel per hectare of forest in 2015. Rationale for including in **se.plan**: a higher level of woodfuel harvest implies greater demand for woodfuel as an energy source, which tends to make forest restoration more feasible when supply of wood to meet local demands is a desired benefit (`view in GEE `__).
+ - Developed by **se.plan** team by downscaling national data from: FAO. 2020. Forestry Production and Trade. In: *FAOSTAT*. http://www.fao.org/faostat/en/#data/FO
+ * - Plantation growth rate (wood production) in **annual dry metric tonnes of woody biomass/hectare**
+ - Potential annual production of woody biomass by fast-growing trees such as eucalypts, poplars and willows. Rationale for including in **se.plan**: faster growth of plantation trees tends to make forest restoration more feasible when desired benefits include income for landholders and wood supply to meet local and export demands (`view in GEE `__).
+ - Albanito, F., Beringer, T., Corstanje, R. *et al.* 2016. Carbon implications of converting cropland to bioenergy crops or forest for climate mitigation: a global assessment. *GCB Bioenergy*, 8: pp. 81–95, https://doi.org/10.1111/gcbb.12242; resolution of Albanito *et al.* layer: 55 km.
.. _seplan-appendix-d:
-Benefit-cost ratio
+Benefit–cost ratio
------------------
In its current form, **se.plan** includes numerical estimates of four categories of potential restoration benefits for each potential restoration site:
-- Biodiversity conservation
-- Carbon sequestration
-- Local livelihoods
-- Wood production.
+- biodiversity conservation
+- carbon sequestration
+- local livelihoods
+- wood production
-Denote these benefits, respectively, by :math:`B_1`, :math:`B_2`, :math:`B_3`, and :math:`B_4`. The data on which the benefit estimates are based have different units. To enable the benefit estimates to be compared to each other, **se.plan** converts them to the same, relative scale, which ranges from 1 (low) to 5 (high). **se.plan** includes two indicators each for :math:`B_1` and :math:`B_3` and a single indicator for :math:`B_2` and :math:`B_4`. We return to this difference in number of indicators below.
+Denote these benefits, respectively, by :math:`B_1`, :math:`B_2`, :math:`B_3`, and :math:`B_4`. The data on which the benefit estimates are based have different units. To enable the benefit estimates to be compared with each other, **se.plan** converts them to the same relative scale, which ranges from 1 (low) to 5 (high). The tool includes two indicators each for :math:`B_1` and :math:`B_3`, and a single indicator for :math:`B_2` and :math:`B_4`. We return to this difference in number of indicators below.
-**se.plan** users rate the relative importance of each benefit on a scale of 1 (low) to 5 (high). **se.plan** treats these ratings as weights and calculates a restoration value index for each site by the weighted-average formula:
+**se.plan** users rate the relative importance of each benefit on a scale of 1 (low) to 5 (high). The tool treats these ratings as weights and calculates a restoration value index for each site by the weighted-average formula:
.. math::
@@ -805,130 +831,129 @@ Denote these benefits, respectively, by :math:`B_1`, :math:`B_2`, :math:`B_3`, a
Where :math:`w_1`, :math:`w_2`, :math:`w_3`, and :math:`w_4` are the user ratings for the four corresponding benefits.
-**se.plan** also includes numerical estimates of restoration cost, defined as the sum of opportunity cost and implementation cost in 2017 US dollars per hectare, for each potential restoration site. **se.plan** calculates an approximate benefit-cost ratio by dividing the restoration value index by the estimate of restoration cost:
+The tool also includes numerical estimates of restoration cost, defined as the sum of opportunity cost and implementation cost expressed in USD per hectare for reference year 2017 for each potential restoration site.
+
+**se.plan** calculates an approximate benefit–cost ratio by dividing the restoration value index by the estimate of restoration cost:
.. math::
Benefit\_cost\_ratio = Restoration\_value\_index / Restoration\_cost.
-The benefit-cost ratio in **se.plan** is approximate in several ways. In particular, **se.plan** does not value potential restoration benefits in monetary terms, and it does not calculate the discounted sum of benefits over a multi-year time period that extends into the future. Its cost estimates account for the future to a greater degree, however; see :ref:`seplan-appendix-e`. As a final step, **se.plan** converts the benefit-cost ratio across all sites in the user’s area of interest to a scale from 1 (low) to 5 (high). It reports this value as the *restoration suitability index* on the map and dashboard.
+The benefit-cost ratio in **se.plan** is approximate in several ways. In particular, the tool does not value potential restoration benefits in monetary terms, and it does not calculate the discounted sum of benefits over a multi-year time period that extends into the future; however, **se.plan's** cost estimates account for the future to a greater degree (see :ref:`seplan-appendix-e`). As a final step, the tool converts the benefit–cost ratio across all sites in the user’s AOI to a scale from 1 (low) to 5 (high), reporting this value as the **Restoration suitability index** on the map and dashboard.
-As noted above, **se.plan** includes two indicators for benefits :math:`B_1` (biodiversity conservation) and :math:`B_3` (local livelihoods). For :math:`B_1`, the two indicators are the *biodiversity intactness index* and *number of endangered species*. Denote these two indicators by :math:`B_1a` and :math:`B_1b`. **se.plan** converts each of these indicators to a 1-5 scale and then calculates the overall biodiversity benefit, :math:`B_1`, as their simple average:
+As noted above, **se.plan** includes two indicators for benefits :math:`B_1` (biodiversity conservation) and :math:`B_3` (local livelihoods). For :math:`B_1`, the two indicators are the **Biodiversity intactness index** and **Number of endangered species** (denote these two indicators by :math:`B_1a` and :math:`B_1b`). The tool converts each of these indicators to a 1–5 scale and then calculates the **Overall biodiversity benefit**, :math:`B_1`, as their simple average:
.. math::
B_1 = (B_1a + B_1b) / 2
-**se.plan** calculates the overall local livelihoods benefit in the same way from its two constituent indicators, *forest employment* and *woodfuel harvest*.
+**se.plan** calculates the overall local livelihoods benefit in the same way from its two constituent indicators, **Forest employment** and **Woodfuel harvest**.
.. _seplan-appendix-e:
Cost data layers
----------------
-In the cases of benefits (:ref:`seplan-appendix-c`) and constraints (:ref:`seplan-appendix-f`), the **se.plan** team adopted the tool’s data layers primarily from existing sources, with little or no modification of the original layers. In contrast, it developed wholly new data layers for both the *opportunity cost* and the *implementation cost* of forest restoration. Developing these layers involved multiple steps, which are described below.
+In the case of benefits (:ref:`seplan-appendix-c`) and constraints (:ref:`seplan-appendix-f`), the **se.plan** team adopted the tool’s data layers primarily from existing sources, with little or no modification of the original layers. In contrast, it developed wholly new data layers for both the opportunity cost and the implementation cost of forest restoration. Developing these layers involved multiple steps, which are described below.
.. note::
- Every data layer presented in the following document can be displayed in Google Earth Engine as an overview of our datasets. Click on the provided link in the description, you'll be redirected to the GEE code editor panel. The selected layer will be displayed over Uganda. To modify the country change the :code:`fao_gaul` variable line 7 by your country number (listed in the Country list section). If you want to export this layer, please set the value of :code:`to_export` (line 10) and :code:`to_drive` (line 13) according to your need.
- Hit the :code:`run` button again to relaunch the computation.
- Code used for this display can be found `here